PATENT DOCUMENT

Publication Number: US-11113510-B1
Application Number: US-201916427948-A
Country: US
Kind Code: B1

Title: Virtual templates for facial recognition

Abstract:
When a device is successfully unlock using a facial recognition authentication process, feature vectors generated from images obtained during the facial recognition authentication process may be stored as temporary templates on the device. After a period of time, one of the temporary templates may be selected to be used as a “virtual” template for the device. For example, a median temporary template in the temporary templates may be selected as the virtual template. The performance of the virtual template may then be assessed over time and compared to the performance of templates generated from an enrollment process to determine if and how the virtual template is implemented on the device.

Claims:
What is claimed is: 
     
       1. A method, comprising:
 obtaining a plurality of images of a face of a user using a camera located on a device, the device comprising a computer processor and a memory, wherein the plurality of images is obtained for user authentication attempts on the device using a facial recognition authentication process; 
 for each obtained image:
 encoding the image to generate at least one feature vector for the image, wherein the at least one feature vector represents one or more facial features of the user in the image; 
 comparing the at least one feature vector to one or more reference templates stored in the memory of the device to obtain a matching score for the at least one feature vector; 
 storing the at least one feature vector as a temporary template in the memory of the device when the matching score for the at least one feature vector is above a threshold; 
 
 after a selected number of temporary templates are stored in the memory of the device, selecting one of the temporary templates based on an assessment of the temporary templates; 
 obtaining a plurality of additional images for additional user authentication attempts of the device using the facial recognition authentication process, 
 assessing, using the plurality of additional images, a performance of the reference templates in the facial recognition authentication process during the additional user authentication attempts of the device; 
 assessing, using the plurality of additional images, a performance of the selected temporary template in an additional facial recognition authentication process during the additional user authentication attempts of the device, 
 comparing the performance of the reference templates and the performance of the selected temporary template during the additional user authentication attempts of the device; and 
 adding the selected temporary template to the reference templates in response to the performance of the selected temporary template being higher than at least one of the reference templates during the additional user authentication attempts of the device. 
 
     
     
       2. The method of  claim 1 , wherein selecting the temporary template comprises selecting a median temporary template from the temporary templates. 
     
     
       3. The method of  claim 1 , wherein selecting the temporary template comprises assessing one or more clusters of the temporary templates in a feature space and selecting a median temporary template in a cluster of temporary templates. 
     
     
       4. The method of  claim 1 , wherein the threshold for storing the feature vector as the temporary template is an unlock threshold for the device. 
     
     
       5. The method of  claim 1 , wherein the selected temporary template replaces one of the reference templates when the performance of the selected temporary template is higher than at least one of the reference templates. 
     
     
       6. The method of  claim 1 , wherein the selected temporary template replaces all the reference templates when the performance of the selected temporary template is higher than each of the reference templates. 
     
     
       7. The method of  claim 1 , wherein the reference templates are generated by:
 obtaining a plurality of reference images of the face of the user using the camera; 
 selecting reference images for encoding based on the selected reference images meeting selected criteria for the reference images; 
 encoding a plurality of facial features of the user from the selected reference images to generate the plurality of reference feature vectors; and 
 storing the plurality of reference feature vectors as the reference templates in the memory of the device. 
 
     
     
       8. The method of  claim 1 , wherein the additional facial recognition authentication process operates alongside the facial recognition authentication process with the selected temporary template replacing the reference templates in the additional facial recognition authentication process. 
     
     
       9. A device, comprising:
 a computer processor; 
 a memory; 
 a camera; 
 at least one illuminator providing infrared illumination; 
 circuitry coupled to the camera and the illuminator, wherein the circuitry is configured to:
 obtain a plurality of images of a face of a user using the camera, wherein the plurality of images is obtained for user authentication attempts on the device using a facial recognition authentication process; 
 for each obtained image:
 encode the image to generate at least one feature vector for the image, wherein the at least one feature vector represents one or more facial features of the user in the image; 
 compare the at least one feature vector to one or more reference templates stored in the memory of the device to obtain a matching score for the at least one feature vector; 
 store the at least one feature vector as a temporary template in the memory of the device when the matching score for the at least one feature vector is above a threshold; 
 
 after a selected number of temporary templates are stored in the memory of the device, select one of the temporary templates based on an assessment of the temporary templates; 
 obtain a plurality of additional images for additional user authentication attempts of the device using the facial recognition authentication process; 
 assess, using the plurality of additional images, a performance of the reference templates in the facial recognition authentication process during the additional user authentication attempts of the device; 
 assess, using the plurality of additional images, a performance of the selected temporary template in an additional facial recognition authentication process during the additional user authentication attempts of the device; 
 compare the performance of the reference templates and the performance of the selected temporary template during the additional user authentication attempts of the device; and 
 add the selected temporary template to the reference templates in response to the performance of the selected temporary template being higher than at least one of the reference templates during the additional user authentication attempts of the device. 
 
 
     
     
       10. The device of  claim 9 , wherein the camera comprises an infrared sensor. 
     
     
       11. The device of  claim 9 , wherein the at least one illuminator comprises a flood infrared illuminator and a pattern infrared illuminator. 
     
     
       12. The device of  claim 9 , wherein the circuitry is configured to perform an enrollment process to generate the reference templates. 
     
     
       13. The device of  claim 12 , wherein the circuitry is, in generating the reference templates, configured to:
 obtain a plurality of reference images of the face of the user using the camera; 
 select reference images for encoding based on the selected reference images meeting selected criteria for the reference images; 
 encode a plurality of facial features of the user from the selected reference images to generate the plurality of reference feature vectors; and 
 store the plurality of reference feature vectors as the reference templates in the memory of the device. 
 
     
     
       14. The device of  claim 9 , wherein the circuitry is configured to select the temporary template by selecting a median temporary template from the temporary templates. 
     
     
       15. The device of  claim 9 , wherein the circuitry is configured to select the temporary template by assessing one or more clusters of the temporary templates in a feature space and selecting a median temporary template in a cluster of temporary templates. 
     
     
       16. The device of  claim 9 , wherein the facial recognition authentication process is operated to authenticate the user as an authorized user of the device. 
     
     
       17. A non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method, comprising, comprising:
 obtaining a plurality of images of a face of a user using a camera located on a device, the device comprising a computer processor and a memory, wherein the plurality of images is obtained for user authentication attempts on the device using a facial recognition authentication process; 
 for each obtained image:
 encoding the image to generate at least one feature vector for the image, wherein the at least one feature vector represents one or more facial features of the user in the image; 
 comparing the at least one feature vector to one or more reference templates stored in the memory of the device to obtain a matching score for the at least one feature vector; 
 storing the at least one feature vector as a temporary template in the memory of the device when the matching score for the at least one feature vector is above a threshold; 
 
 after a selected number of temporary templates are stored in the memory of the device, selecting one of the temporary templates based on an assessment of the temporary templates; 
 obtaining a plurality of additional images for additional user authentication attempts of the device using the facial recognition authentication process; 
 assessing, using the plurality of additional images, a performance of the reference templates in the facial recognition authentication process during the additional user authentication attempts of the device; 
 assessing, using the plurality of additional images, a performance of the selected temporary template in an additional facial recognition authentication process during the additional user authentication attempts of the device, 
 comparing the performance of the reference templates and the performance of the selected temporary template during the additional user authentication attempts of the device; and 
 adding the selected temporary template to the reference templates in response to the performance of the selected temporary template being higher than at least one of the reference templates during the additional user authentication attempts of the device. 
 
     
     
       18. The non-transient computer-readable medium of  claim 17 , wherein the facial recognition authentication process comprises:
 obtaining at least one user authentication attempt image of the user using the camera located on the device; 
 encoding the at least one user authentication attempt image to generate at least one user authentication attempt feature vector, wherein the user authentication attempt feature vector represents one or more facial features of the user in the at least one user authentication attempt image; 
 comparing the at least one user authentication attempt feature vector to the reference templates to obtain a first matching score; 
 comparing the at least one user authentication attempt feature vector to the selected temporary template to obtain a second matching score; and 
 authorizing the user to perform at least one operation on the device that requires authentication when either the first matching score or the second matching score is above an unlock threshold. 
 
     
     
       19. The non-transient computer-readable medium of  claim 17 , wherein the images are infrared images of the face of the user. 
     
     
       20. The non-transient computer-readable medium of  claim 17 , wherein assessing the performance of the reference templates and the performance of the selected temporary template comprises assessing acceptance rates and rejection rates of the plurality of additional images obtained during the additional user authentication attempts of the device.

Description:
PRIORITY CLAIM 
     This patent claims priority to U.S. Provisional Patent Application No. 62/679,849 to Mostafa et al., entitled “VIRTUAL TEMPLATES FOR FACIAL RECOGNITION”, filed Jun. 3, 2018, which is incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     1. Technical Field 
     Embodiments described herein relate to methods and systems for face detection and recognition in images captured by a camera on a device. More particularly, embodiments described herein relate to the enrollment of a profile for face recognition of the user and methods for updating the profile based on the user&#39;s behavior during use of the device. 
     2. Description of Related Art 
     Biometric authentication processes are being used more frequently to allow users to more readily access their devices without the need for passcode or password authentication. One example of a biometric authentication process is fingerprint authentication using a fingerprint sensor. Facial recognition is another biometric process that may be used for authentication of an authorized user of a device. Facial recognition processes are generally used to identify individuals in an image and/or compare individuals in images to a database of individuals to match the faces of individuals. 
     For authentication using facial recognition, an authorized user typically follows an enrollment protocol to register the user&#39;s face on the device for future unlocking of the device using facial recognition authentication. The enrollment protocol typically has the user follow a controlled regiment to capture the user&#39;s face in different poses and/or positions in order to provide the best possible experience for the user in unlocking the device. Generally, however, many users operate the device with different behavior than is exhibited during the enrollment process. Thus, there may be potential for the device to provide an even better user experience by generating additional protocols for unlocking the device based on the user&#39;s behavior in actual operation of the device after enrollment. 
     SUMMARY 
     Templates for facial recognition may be generated from enrollment images of the user obtained by a camera associated with a device. The enrollment images may be captured during an enrollment process on the device. The templates generated from enrollment may be added to a template space in the device for use during a facial recognition authentication process. After enrollment, the user may attempt to gain access to (e.g., unlock) the device using the facial recognition authentication process. During the facial recognition authentication process, a captured image of the user may be encoded to generate feature vectors for an “unlock” image (e.g., an image captured to unlock the device) that are then compared to the template space. 
     If the unlock image is successful in unlocking the device, the generated feature vectors may be stored as temporary templates in the device. Additional temporary templates may be added as the user continues to successfully unlock the device using the facial recognition authentication process. After a number of temporary templates have been added, the temporary templates may be assessed to select a “virtual” template from the temporary templates. The virtual template may be, for example, a median template in a cluster of the temporary templates in a feature space. As the user continues to use the device, the performance of the virtual template in the facial recognition authentication process may be tracked or monitored over a period of time. The performance of the virtual template in the facial recognition authentication process may be compared to the performance of the templates in the template space to determine if the virtual template is to be added to the template space for future operation of the device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features and advantages of the methods and apparatus of the embodiments described in this disclosure will be more fully appreciated by reference to the following detailed description of presently preferred but nonetheless illustrative embodiments in accordance with the embodiments described in this disclosure when taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a representation of an embodiment of a device including a camera. 
         FIG. 2  depicts a representation of an embodiment of a camera. 
         FIG. 3  depicts a representation of an embodiment of a processor on a device. 
         FIG. 4  depicts a flowchart of an embodiment of an image enrollment process for an authorized user of a device. 
         FIG. 5  depicts a representation of an embodiment of a feature space with feature vectors after an enrollment process. 
         FIG. 6  depicts a representation of an embodiment of a template space for an enrollment profile in a memory of a device. 
         FIG. 7  depicts a flowchart of an embodiment of a facial recognition authentication process. 
         FIG. 8  depicts a flowchart of an embodiment of a “virtual” template process. 
         FIG. 9  depicts a representation of an embodiment of a feature space with temporary templates. 
         FIG. 10  depicts a flowchart of another embodiment of a facial recognition authentication process. 
         FIG. 11  depicts a flowchart of an embodiment of a template update process. 
         FIG. 12  depicts a representation of an embodiment of a template space represented as a feature space. 
         FIG. 13  depicts a flowchart of an embodiment of a template update sub-process. 
         FIG. 14  depicts a flowchart of an additional embodiment of a template update process. 
         FIG. 15  depicts a representation of an additional embodiment of a template space represented as a feature space. 
         FIG. 16  depicts a block diagram of one embodiment of an exemplary computer system. 
         FIG. 17  depicts a block diagram of one embodiment of a computer accessible storage medium. 
     
    
    
     While embodiments described in this disclosure may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the embodiments to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the appended claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description. As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to. 
     Various units, circuits, or other components may be described as “configured to” perform a task or tasks. In such contexts, “configured to” is a broad recitation of structure generally meaning “having circuitry that” performs the task or tasks during operation. As such, the unit/circuit/component can be configured to perform the task even when the unit/circuit/component is not currently on. In general, the circuitry that forms the structure corresponding to “configured to” may include hardware circuits and/or memory storing program instructions executable to implement the operation. The memory can include volatile memory such as static or dynamic random access memory and/or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware circuits may include any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random access memory or embedded dynamic random access memory, custom designed circuitry, programmable logic arrays, etc. Similarly, various units/circuits/components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including the phrase “configured to.” Reciting a unit/circuit/component that is configured to perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation for that unit/circuit/component. 
     In an embodiment, hardware circuits in accordance with this disclosure may be implemented by coding the description of the circuit in a hardware description language (HDL) such as Verilog or VHDL. The HDL description may be synthesized against a library of cells designed for a given integrated circuit fabrication technology, and may be modified for timing, power, and other reasons to result in a final design database that may be transmitted to a foundry to generate masks and ultimately produce the integrated circuit. Some hardware circuits or portions thereof may also be custom-designed in a schematic editor and captured into the integrated circuit design along with synthesized circuitry. The integrated circuits may include transistors and may further include other circuit elements (e.g. passive elements such as capacitors, resistors, inductors, etc.) and interconnect between the transistors and circuit elements. Some embodiments may implement multiple integrated circuits coupled together to implement the hardware circuits, and/or discrete elements may be used in some embodiments. 
     The scope of the present disclosure includes any feature or combination of features disclosed herein (either explicitly or implicitly), or any generalization thereof, whether or not it mitigates any or all of the problems addressed herein. Accordingly, new claims may be formulated during prosecution of this application (or an application claiming priority thereto) to any such combination of features. In particular, with reference to the appended claims, features from dependent claims may be combined with those of the independent claims and features from respective independent claims may be combined in any appropriate manner and not merely in the specific combinations enumerated in the appended claims. 
     DETAILED DESCRIPTION OF EMBODIMENTS 
     This specification includes references to “one embodiment” or “an embodiment.” The appearances of the phrases “in one embodiment” or “in an embodiment” do not necessarily refer to the same embodiment, although embodiments that include any combination of the features are generally contemplated, unless expressly disclaimed herein. Particular features, structures, or characteristics may be combined in any suitable manner consistent with this disclosure. 
       FIG. 1  depicts a representation of an embodiment of a device including a camera. In certain embodiments, device  100  includes camera  102 , processor  104 , memory  106 , and display  108 . Device  100  may be a small computing device, which may be, in some cases, small enough to be handheld (and hence also commonly known as a handheld computer or simply a handheld). In certain embodiments, device  100  is any of various types of computer systems devices which are mobile or portable and which perform wireless communications using WLAN communication (e.g., a “mobile device”). Examples of mobile devices include mobile telephones or smart phones, and tablet computers. Various other types of devices may fall into this category if they include wireless or RF communication capabilities (e.g., Wi-Fi, cellular, and/or Bluetooth), such as laptop computers, portable gaming devices, portable Internet devices, and other handheld devices, as well as wearable devices such as smart watches, smart glasses, headphones, pendants, earpieces, etc. In general, the term “mobile device” can be broadly defined to encompass any electronic, computing, and/or telecommunications device (or combination of devices) which is easily transported by a user and capable of wireless communication using, for example, WLAN, Wi-Fi, cellular, and/or Bluetooth. In certain embodiments, device  100  includes any device used by a user with processor  104 , memory  106 , and display  108 . Display  108  may be, for example, an LCD screen or touchscreen. In some embodiments, display  108  includes a user input interface for device  100  (e.g., the display allows interactive input for the user). 
     Camera  102  may be used to capture images of the external environment of device  100 . In certain embodiments, camera  102  is positioned to capture images in front of display  108 . Camera  102  may be positioned to capture images of the user (e.g., the user&#39;s face) while the user interacts with display  108 .  FIG. 2  depicts a representation of an embodiment of camera  102 . In certain embodiments, camera  102  includes one or more lenses and one or more image sensors  103  for capturing digital images. Digital images captured by camera  102  may include, for example, still images, video images, and/or frame-by-frame images. 
     In certain embodiments, camera  102  includes image sensor  103 . Image sensor  103  may be, for example, an array of sensors. Sensors in the sensor array may include, but not be limited to, charge coupled device (CCD) and/or complementary metal oxide semiconductor (CMOS) sensor elements to capture infrared images (IR) or other non-visible electromagnetic radiation. In some embodiments, camera  102  includes more than one image sensor to capture multiple types of images. For example, camera  102  may include both IR sensors and RGB (red, green, and blue) sensors. In certain embodiments, camera  102  includes illuminators  105  for illuminating surfaces (or subjects) with the different types of light detected by image sensor  103 . For example, camera  102  may include an illuminator for visible light (e.g., a “flash illuminator), illuminators for RGB light, and/or illuminators for infrared light (e.g., a flood IR source and a pattern (speckle pattern) projector). In some embodiments, the flood IR source and pattern projector are other wavelengths of light (e.g., not infrared). In certain embodiments, illuminators  105  include an array of light sources such as, but not limited to, VCSELs (vertical-cavity surface-emitting lasers). In some embodiments, image sensors  103  and illuminators  105  are included in a single chip package. In some embodiments, image sensors  103  and illuminators  105  are located on separate chip packages. 
     In certain embodiments, image sensor  103  is an IR image sensor and the image sensor is used to capture infrared images used for face detection, facial recognition authentication, and/or depth detection. Other embodiments of image sensor  103  (e.g., an RGB image sensor) may also be contemplated for use in face detection, facial recognition authentication, and/or depth detection as described herein. For face detection, illuminator  105 A may provide flood IR illumination to flood the subject with IR illumination (e.g., an IR flashlight) and image sensor  103  may capture images of the flood IR illuminated subject. Flood IR illumination images may be, for example, two-dimensional images of the subject illuminated by IR light. 
     Depth information may be captured using any suitable depth imaging system, examples of which include structured light and time of flight systems. In some instances, the depth imaging system may utilize an illuminator in providing depth detection or generating a depth map image. For example, illuminator  105 B may provide IR illumination with a pattern (e.g., patterned infrared (IR) illumination). The pattern may be a pattern of light with a known, and controllable, configuration and pattern projected onto a subject (e.g., a structured pattern of light). In certain embodiments, the pattern is a speckle pattern (e.g., a pattern of dots). The pattern may, however, include any structured or semi-structured pattern of light features. For example, the pattern may include, but not be limited to, dots, speckles, stripes, dashes, nodes, edges, and combinations thereof. 
     Illuminator  105 B may include a VCSEL array configured to form the pattern or a light source and patterned transparency configured to form the pattern. The configuration and pattern of the pattern provided by illuminator  105 B may be selected, for example, based on a desired pattern density (e.g., speckle or dot density) at the subject. Image sensor  103  may capture images of the subject illuminated by the pattern. The captured image of the pattern on the subject may be assessed (e.g., analyzed and/or processed) by an imaging and processing system (e.g., an image signal processor (ISP) as described herein) to produce or estimate a three-dimensional map of the subject (e.g., a depth map or depth map image of the subject). Examples of depth map imaging are described in U.S. Pat. No. 8,150,142 to Freedman et al., U.S. Pat. No. 8,749,796 to Pesach et al., and U.S. Pat. No. 8,384,997 to Shpunt et al., which are incorporated by reference as if fully set forth herein, and in U.S. Patent Application Publication No. 2016/0178915 to Mor et al., which is incorporated by reference as if fully set forth herein. 
     In certain embodiments, images captured by camera  102  include images with the user&#39;s face (e.g., the user&#39;s face is included in the images). An image with the user&#39;s face may include any digital image with at least some portion of the user&#39;s face shown within the frame of the image. Such an image may include just the user&#39;s face or may include the user&#39;s face in a smaller part or portion of the image. The user&#39;s face may be captured with sufficient resolution in the image to allow image processing of one or more features of the user&#39;s face in the image. 
     Images captured by camera  102  may be processed by processor  104 .  FIG. 3  depicts a representation of an embodiment of processor  104  included in device  100 . Processor  104  may include circuitry configured to execute instructions defined in an instruction set architecture implemented by the processor. Processor  104  may execute the main control software of device  100 , such as an operating system. Generally, software executed by processor  104  during use may control the other components of device  100  to realize the desired functionality of the device. The processors may also execute other software. These applications may provide user functionality, and may rely on the operating system for lower-level device control, scheduling, memory management, etc. 
     In certain embodiments, processor  104  includes image signal processor (ISP)  110 . ISP  110  may include circuitry suitable for processing images (e.g., image signal processing circuitry) received from camera  102 . ISP  110  may include any hardware and/or software (e.g., program instructions) capable of processing or analyzing images captured by camera  102 . 
     In certain embodiments, processor  104  includes secure enclave processor (SEP)  112 . In some embodiments, SEP  112  is involved in a facial recognition authentication process involving images captured by camera  102  and processed by ISP  110 . SEP  112  may be a secure circuit configured to authenticate an active user (e.g., the user that is currently using device  100 ) as authorized to use device  100 . A “secure circuit” may be a circuit that protects an isolated, internal resource from being directly accessed by an external circuit. The internal resource may be memory (e.g., memory  106 ) that stores sensitive data such as personal information (e.g., biometric information, credit card information, etc.), encryptions keys, random number generator seeds, etc. The internal resource may also be circuitry that performs services/operations associated with sensitive data. As described herein, SEP  112  may include any hardware and/or software (e.g., program instructions) capable of authenticating a user using the facial recognition authentication process. The facial recognition authentication process may authenticate a user by capturing images of the user with camera  102  and comparing the captured images to previously collected images of an authorized user for device  100 . In some embodiments, the functions of ISP  110  and SEP  112  may be performed by a single processor (e.g., either ISP  110  or SEP  112  may perform both functionalities and the other processor may be omitted). 
     In certain embodiments, processor  104  performs an enrollment process (e.g., image enrollment process  200 , as shown in  FIG. 4 , or a registration process) to capture images (e.g., the previously collected images) for an authorized user of device  100 . During the enrollment process, camera module  102  may capture (e.g., collect) images and/or image data from an authorized user in order to permit SEP  112  (or another security process) to subsequently authenticate the user using the facial recognition authentication process. In some embodiments, the images and/or image data (e.g., feature vector data from the images) from the enrollment process are used to generate templates in device  100 . The templates may be stored, for example, in a template space in memory  106  of device  100 . In some embodiments, the template space may be updated by the addition and/or subtraction of templates from the template space. A template update process (e.g., first template update process  300  and/or second template update process  400  described herein) may be performed by processor  104  to add and/or subtract templates from the template space. For example, the template space may be updated with additional templates to adapt to changes in the authorized user&#39;s appearance and/or changes in hardware performance over time. Templates may be subtracted from the template space to compensate for the addition of templates when the template space for storing templates is full. 
     In some embodiments, camera module  102  captures multiple pairs of images for a facial recognition session. Each pair may include an image captured using a two-dimensional capture mode (e.g., a flood IR image) and an image captured using a three-dimensional capture mode (e.g., a patterned illumination image used to generate a depth map image). In certain embodiments, ISP  110  and/or SEP  112  process the flood IR images and patterned illumination images independently of each other before a final authentication decision is made for the user. For example, ISP  110  may process the images independently to determine characteristics of each image separately. SEP  112  may then compare the separate image characteristics with stored templates for each type of image to generate an authentication score (e.g., a matching score or other ranking of matching between the user in the captured image and in the stored templates) for each separate image. The authentication scores for the separate images (e.g., the flood IR and patterned illumination images) may be combined to make a decision on the identity of the user and, if authenticated, allow the user to use device  100  (e.g., unlock the device). 
     In some embodiments, ISP  110  and/or SEP  112  combine the images in each pair to provide a composite image that is used for facial recognition. In some embodiments, ISP  110  processes the composite image to determine characteristics of the image, which SEP  112  may compare with the stored templates to make a decision on the identity of the user and, if authenticated, allow the user to use device  100 . 
     In some embodiments, the combination of flood IR image data and patterned illumination image data may allow for SEP  112  to compare faces in a three-dimensional space. In some embodiments, camera module  102  communicates image data to SEP  112  via a secure channel. The secure channel may be, for example, either a dedicated path for communicating data (i.e., a path shared by only the intended participants) or a dedicated path for communicating encrypted data using cryptographic keys known only to the intended participants. In some embodiments, camera module  102  and/or ISP  110  may perform various processing operations on image data before supplying the image data to SEP  112  in order to facilitate the comparison performed by the SEP. 
     In certain embodiments, processor  104  operates one or more machine learning models. Machine learning models may be operated using any combination of hardware and/or software (e.g., program instructions) located in processor  104  and/or on device  100 . In some embodiments, one or more neural network modules  114  are used to operate the machine learning models on device  100 . Neural network modules  114  may be located in ISP  110  and/or SEP  112 . 
     Neural network module  114  may include any combination of hardware and/or software (e.g., program instructions) located in processor  104  and/or on device  100 . In some embodiments, neural network module  114  is a multi-scale neural network or another neural network where the scale of kernels used in the network can vary. In some embodiments, neural network module  114  is a recurrent neural network (RNN) such as, but not limited to, a gated recurrent unit (GRU) recurrent neural network or a long short-term memory (LSTM) recurrent neural network. 
     Neural network module  114  may include neural network circuitry installed or configured with operating parameters that have been learned by the neural network module or a similar neural network module (e.g., a neural network module operating on a different processor or device). For example, a neural network module may be trained using training images (e.g., reference images) and/or other training data to generate operating parameters for the neural network circuitry. The operating parameters generated from the training may then be provided to neural network module  114  installed on device  100 . Providing the operating parameters generated from training to neural network module  114  on device  100  allows the neural network module to operate using training information programmed into the neural network module (e.g., the training-generated operating parameters may be used by the neural network module to operate on and assess images captured by the device). 
       FIG. 4  depicts a flowchart of an embodiment of image enrollment process  200  for an authorized user of device  100 . Process  200  may be used to create an enrollment profile for an authorized user of device  100  that is stored in the device (e.g., in a memory coupled to SEP  112 ). The enrollment profile may include one or more templates for the authorized user created using process  200 . The enrollment profile and the templates associated with the enrollment profile may be used in a facial recognition process to allow (e.g., authorize) the user to use the device and/or perform operations on the device (e.g., unlock the device). 
     In certain embodiments, process  200  is used when device  100  is used a first time by the authorized user and/or when the user opts to create an enrollment profile for a facial recognition process. For example, process  200  may be initiated when device  100  is first obtained by the authorized user (e.g., purchased by the authorized user) and turned on for the first time by the authorized user. In some embodiments, process  200  may be initiated by the authorized user when the user desires to enroll in a facial recognition process, update security settings for device  100 , re-enroll, and/or add an enrollment profile on the device. 
     In certain embodiments, process  200  begins with authenticating the user in  202 . In  202 , the user may be authenticated on device  100  using a non-facial authentication process. For example, the user may be authenticated as an authorized user by entering a passcode, entering a password, or using another user authentication protocol other than facial recognition. After the user is authenticated in  202 , one or more enrollment (e.g., reference or registration) images of the user are captured in  204 . The enrollment images may include images of the user illuminated by flood illuminator  105 A (e.g., flood IR images) and/or images of the user illuminated by illuminator  105 B (e.g., patterned illumination images). As described herein, flood IR images and patterned illumination images may be used independently and/or in combination in facial recognition processes on device  100  (e.g. the images may independently be used to provide an authentication decision and the decisions may be combined to determine a final decision on user authentication). 
     The enrollment images may be captured using camera  102  as the user interacts with device  100 . For example, the enrollment images may be captured as the user follows prompts on display  108  of device  100 . The prompts may include instructions for the user to make different motions and/or poses while the enrollment images are being captured. During  204 , camera  102  may capture multiple images for each motion and/or pose performed by the user. Capturing images for different motions and/or different poses of the user where the images still have a relatively clear depiction of the user may be useful in providing a better variety of enrollment images that enable the user to be authenticated without having to be in a limited or restricted position relative to camera  102  on device  100 . 
     After the multiple enrollment images are captured in  204 , selection of enrollment images for further image processing may be made in  206 . Selection of enrollment images  206 , and further processing of the images, may be performed by ISP  110  and/or SEP  112 . Selection of enrollment images for further processing may include selecting images that are suitable for generating templates. For example, the selection of images that are suitable for use generating templates in  206  may include assessing one or more selected criteria for the images and selecting images that meet the selected criteria. The selected images may be used to generate templates for the user. Selected criteria may include, but not be limited to, the face of the user being in the field of view of the camera, a pose of the user&#39;s face being proper (e.g., the user&#39;s face is not turned to far in any direction from the camera (i.e., the pitch, yaw, and/or roll of the face are not above certain levels), a distance between camera  102  and the face of the user being in a selected distance range, the face of the user having occlusion below a minimum value (e.g., the user&#39;s face is not occluded (blocked) more than a minimum amount by another object), the user paying attention to the camera (e.g., eyes of the user looking at the camera), eyes of the user not being closed, and proper lighting (illumination) in the image. In some embodiments, if more than one face is detected in an enrollment image, the enrollment image is rejected and not used (e.g., not selected) for further processing. Selection of images suitable for further processing may be rule based on the images meeting a certain number of the selected criteria or all of the selected criteria. In some embodiments, occlusion maps and/or landmark feature maps are used in identifying features of the user (e.g., facial features such as eyes, nose, and mouth) in the images and assessing the selected criteria in the images. 
     After images are selected in  206 , features of the user in the selected (template) images may be encoded in  208 . Encoding of the selected images may include encoding features (e.g., facial features) of the user to define the features in the images as one or more feature vectors in a feature space. Feature vectors  210  may be the output of the encoding in  208 . A feature space may be an n-dimensional feature space. A feature vector may be an n-dimensional vector of numerical values that define features from the image in the feature space (e.g., the feature vector may be a vector of numerical values that define facial features of the user in the image). 
       FIG. 5  depicts a representation of an embodiment of feature space  212  with feature vectors  210 . Each feature vector  210  (black dot) may define facial features for the user from either a single image, from a composite image (e.g., an image that is a composite of several images), or from multiple images. As feature vectors  210  are generated from a single user&#39;s facial features, the feature vectors may be similar to one another because the feature vectors are associated with the same person and may have some “clustering”, as shown by circle  211  in  FIG. 5 . Feature vectors  256 A and  256 B (open diamonds) are feature vectors obtained from facial recognition process  250 , described below. 
     As shown in  FIG. 4 , process  200  may include, in  214 , storing feature vectors  210  in a memory of device  100  (e.g., a memory protected by SEP  112 ). In certain embodiments, feature vectors  210  are stored as static templates  216  (e.g., enrollment templates or reference templates) in a template space of the memory (e.g., template space  220  described below). Static templates  216  may be used for the enrollment profile created by process  200 . In some embodiments, static templates  216  (and other templates described herein) include separate templates for feature vectors obtained from the enrollment flood IR images and for feature vectors obtained from the enrollment patterned illumination images. It is to be understood that the separate templates obtained from flood IR images and patterned illumination images (e.g., images used to generate depth map images) may be used independently and/or in combination during additional processes described herein. For simplicity in this disclosure, static templates  216  are described generically and it should be understood that static templates  216  (and the use of the templates) may refer to either templates obtained from flood IR images or templates obtained from patterned illumination images. In some embodiments, a combination of the flood IR images and patterned illumination images may be used to generate templates. For example, pairs of feature vectors obtained from flood IR images and patterned illumination images may be stored in static templates  216  to be used in one or more facial recognition processes on device  100 . 
       FIG. 6  depicts a representation of an embodiment of template space  220  for an enrollment profile in memory  106  of device  100 . In certain embodiments, template space  220  is located in a portion of memory  106  of device  100  protected by SEP  112 . In some embodiments, template space  220  includes static portion  222  and dynamic portion  224 . Static templates  216  may be, for example, added to static portion  222  of template space  220  (e.g., the templates are permanently added to the memory and are not deleted or changed unless the device is reset). In some embodiments, static portion  222  includes a certain number of static templates  216 . For example, for the embodiment of template space  220  depicted in  FIG. 6 , six static templates  216  are allowed in static portion  222 . In some embodiments, nine static templates  216  may be allowed in static portion  222 . Other numbers of static templates  216  in static portion  222  may also be contemplated. After the enrollment process for the enrollment profile is completed and static templates  216  are added to static portion  222 , additional dynamic templates  226  may be added to dynamic portion  224  of template space  220  for the enrollment profile (e.g., a portion from which templates may be added and deleted without a device reset being needed). 
     Static templates  216  may thus be enrollment templates (or reference templates) generated by enrollment process  200  for the enrollment profile associated with the enrollment process. After enrollment process  200  is completed, a selected number of static templates  216  are stored in static portion  222  of template space  220  for the enrollment profile. The number of static templates  216  stored in static portion  222  after enrollment process  200  may vary depending on, for example, the number of different feature vectors obtained during the enrollment process, which may be based on the number of images selected to be suitable for use as template images, or a desired number of templates for the device. After enrollment process  200 , static templates  216  include feature vectors  210  (e.g., the enrollment or reference feature vectors) that can be used for facial recognition of the authorized user associated with the enrollment profile. Thus, template space  220  may be used in a facial recognition authentication process to authorize the user associated with the enrollment profile. 
       FIG. 7  depicts a flowchart of an embodiment of facial recognition authentication process  250 . Process  250  may be used to authenticate a user as an authorized user of device  100  using facial recognition of the user. In certain embodiments, process  250  is used to authenticate a user using an enrollment profile (e.g., template space  220 ) on device  100 . Authentication of the authorized user may allow the user to access and use device  100  (e.g., unlock the device) and/or have access to a selected functionality of the device (e.g., unlocking a function of an application running on the device, payment systems (i.e., making a payment), access to personal data, expanded view of notifications, etc.). In certain embodiments, process  250  is used as a primary biometric authentication process for device  100  (after enrollment of the authorized user). In some embodiments, process  250  is used as an authentication process in addition to another authentication process (e.g., fingerprint authentication, another biometric authentication, passcode entry, password entry, and/or pattern entry). In some embodiments, another authentication process (e.g., passcode entry, pattern entry, other biometric authentication) may be used to access device  100  if the user fails to be authenticated using process  250 . 
     In  252 , camera  102  captures an image of the face of the user attempting to be authenticated for access to device  100  (e.g., the camera captures an “unlock attempt” image of the user). It is to be understood that the unlock attempt image may be a single image of the face of the user (e.g., a single flood IR image or single patterned illumination image) or the unlock attempt image may be a series of several images of the face of the user taken over a short period of time (e.g., one second or less). In some embodiments, the series of several images of the face of the user includes pairs of flood IR images and patterned illumination images (e.g., pairs of consecutive flood IR and patterned illumination images). In some implementations, the unlock attempt image may be a composite of several images of the user illuminated by the flood illuminator and the pattern illuminator. 
     Camera  102  may capture the unlock attempt image in response to a prompt by the user or a user generated request. For example, the unlock attempt image may be captured when the user attempts to access to device  100  by pressing a button (e.g., a home button or virtual button) on device  100 , by moving the device into a selected position relative to the user&#39;s face (e.g., the user moves the device such that the camera is pointed directly at the user&#39;s face), and/or by making a specific gesture or movement with respect to the device (e.g., tapping on the screen, swiping the user&#39;s finger across the display, or picking the device off the table). It is to be understood that, as described herein, unlock attempt images may include either flood IR images or patterned illumination images, or a combination thereof. Further, the unlock attempt images may be processed in association with their corresponding template (e.g., flood IR images with a template for flood IR enrollment images) independently or in combination as needed. 
     Additionally, unlock attempt images may include images that have been processed using a face detection process to determine and locate one or more faces in the images. Images that are further processed in process  250  (e.g., encoded in  254 ) may include only images in which at least one face has been detected. If no face is detected in an unlock attempt image, the unlock attempt image may be discarded and additional unlock attempt images may be captured (either automatically or after user input) until a face is detected in the captured unlock attempt images. Examples of face detection processes are described in U.S. patent application Ser. No. 15/910,551 to Gernoth et al. and U.S. Provisional Patent Application No. 62/679,850 to Kumar et al., which are incorporated by reference as if fully set forth herein. 
     In  254 , the unlock attempt image is encoded to define the facial features of the user as one or more feature vectors in the feature space. In some embodiments, one feature vector is defined for the unlock attempt image. In some embodiments, multiple feature vectors are defined for the unlock attempt image. Unlock feature vector(s)  256  may be the output of the encoding of the unlock attempt image in  254 . 
     In certain embodiments, in  258 , unlock feature vector(s)  256  are compared to feature vectors in the templates of template space  220  to get matching score  260  for the unlock attempt image. In certain embodiments, template space  220  is the template space for an enrollment profile on device  100 . Matching score  260  may be a score of the differences between feature vector(s)  256  and feature vectors in template space  220  (e.g., feature vectors in static templates  216  and/or other dynamic templates  226  added to the template space as described herein). The closer (e.g., the less distance or less differences) that feature vector(s)  256  and the feature vectors in template space  220  are, the higher matching score  260  may be. For example, as shown in  FIG. 5 , feature vector  256 A (open diamond) is closer to feature vectors  210  than feature vector  256 B (open diamond)(e.g., feature vector  256 B is a further outlier than feature vector  256 A). Thus, feature vector  256 A would have a higher matching score than feature vector  256 B. As feature vector  256 B is further away from feature vectors  210  than feature vector  256 A, the lower matching score for feature vector  256 B means less confidence that the face in the unlock attempt image associated with feature vector  256 B is the face of the authorized user associated with the enrollment profile and template space  220 . 
     In some embodiments, comparing feature vector(s)  256  and templates from template space  220  to get matching score  260  includes using one or more classifiers or a classification-enabled network to classify and evaluate the differences between feature vector(s)  256  and templates from template space  220 . Examples of different classifiers that may be used include, but are not limited to, linear, piecewise linear, nonlinear classifiers, support vector machines, and neural network classifiers. In some embodiments, matching score  260  is assessed using distance scores between feature vector(s)  256  and templates from template space  220 . 
     In  262 , matching score  260  is compared to unlock threshold  264  for device  100 . Unlock threshold  264  may represent a minimum difference (e.g., distance in the feature space) in features (as defined by feature vectors) between the face of the authorized user and the face of the user in the unlock attempt image that device  100  requires in order to unlock the device (or unlock a feature on the device). For example, unlock threshold  264  may be a threshold value that determines whether the unlock feature vectors (e.g., feature vectors  256 ) are similar enough (e.g., close enough) to the templates associated with the authorized user&#39;s face (e.g., static templates  216  in template space  220 ). As further example, unlock threshold  264  may be represented by circle  265  in feature space  212 , depicted in  FIG. 5 . As shown in  FIG. 5 , feature vector  256 A is inside circle  265  and thus feature vector  256 A would have matching score  260  above unlock threshold  264 . Feature vector  256 B, however, is outside circle  265  and thus feature vector  256 B would have matching score  260  below unlock threshold  264 . In certain embodiments, unlock threshold  264  is set during manufacturing and/or by the firmware of device  100 . In some embodiments, unlock threshold  264  is updated (e.g., adjusted) by device  100  during operation of the device as described herein. 
     As shown in  FIG. 7 , in  262 , if matching score  260  is above unlock threshold  264  (i.e., the user&#39;s face in the unlock attempt image substantially matches the face of the authorized user), the user in the unlock attempt image is authenticated as the authorized user for the enrollment profile on device  100  and the device is unlocked in  266 . In some embodiments, after device  100  is unlocked in  266 , unlock feature vectors  256  are provided to “virtual” template process  700 , shown in  FIG. 8 . In certain embodiments, after device  100  is unlocked in  266 , unlock feature vectors  256  and matching score  260  are provided to first template update process  300 , shown in  FIG. 11 , which may add or replace templates in template space  220 . 
     In  262 , if matching score  260  is below unlock threshold  264  (e.g., not equal to or above the unlock threshold), then device  100  is not unlocked in  268  (e.g., the device remains locked). It should be noted that device  100  may be either locked or unlocked if matching score  260  is equal to unlock threshold  264  depending on a desired setting for the unlock threshold (e.g., tighter or looser restrictions). Additionally, either option for an equal matching score comparison may be also applied as desired for other embodiments described herein. 
     In certain embodiments, the unlock attempts are compared to a threshold in  270 . The threshold may be, for example, a maximum number of unlock attempts allowed or a maximum allotted time for unlock attempts. In certain embodiments, a number of unlock attempts is counted (e.g., the number of attempts to unlock device  100  with a different unlock attempt image captured in  252 ) and compared to the maximum number of unlock attempts allowed. 
     In certain embodiments, if the unlock attempts reaches the threshold (e.g., number of unlock attempts reaches the maximum number of attempts allowed), then device  100  is locked from further attempts to use facial authentication in  272 . In some embodiments, when the device is locked in  272 , an error message may be displayed (e.g., on display  108 ) indicating that facial recognition authentication process  250  has failed and/or the desired operation of device  100  is restricted or prevented from being performed. Device  100  may be locked from further attempts to use facial authentication in  272  for a specified period of time and/or until another authentication protocol is used to unlock the device. For example, unlock options  274  may be used to unlock device  100 . 
     Unlock options  274  may include the user being presented with one or more options for proceeding with a different type of authentication to unlock or access features on device  100  (e.g., the user is presented options for proceeding with a second authentication protocol). Presenting the options may include, for example, displaying one or more options on display  108  of device  100  and prompting the user through audible and/or visual communication to select one of the displayed options to proceed with unlocking the device or accessing features on the device. The user may then proceed with unlocking/accessing device  100  using the selected option and following additional audible and/or visual prompts as needed. After successfully being authenticated using the selected option, the user&#39;s initial request for unlocking/accessing device  100  may be granted. Unlock options  274  may include, but not be limited to, using a passcode, a password, pattern entry, a different form of biometric authentication, or another authentication protocol to unlock device  100 . In some embodiments, unlock options  274  includes providing a “use passcode/password/pattern” affordance that, when selected causes display of a passcode/password/pattern entry user interface, or a passcode/password/pattern entry user interface, or a “use fingerprint” prompt that, when displayed, prompts the user to place a finger on a fingerprint sensor for the device. In some embodiments, after device  100  is unlocked using the unlock options in  274 , unlock feature vectors  256  and matching score  260  are provided to second template update process  400 , shown in  FIG. 14 . 
     If the unlock attempts are below the threshold in  270  (e.g., number of unlock attempts are below the maximum number of attempts allowed), then process  250  may be run again (re-initiated) beginning with a new unlock attempt image of the user being captured in  252 . In some implementations, device  100  automatically captures the new unlock attempt image of the user&#39;s face without prompting the user (e.g., capturing of the new image is automatically implemented and/or hidden from the user). In some implementations, device  100  notifies the user (either visually and/or audibly) that process  250  is being re-initiated. In some embodiments, device  100  may prompt the user to provide input to re-initiate process  250 . For example, the user may be prompted to acknowledge or otherwise confirm (either visually and/or audibly) the attempt to re-initiate process  250 . 
       FIG. 8  depicts a flowchart of an embodiment of “virtual” template process  700 . Process  700  may be used to add a “virtual” static template to template space  220 . The virtual static template may be implemented into future operations of a facial recognition authentication process and the performance of the virtual static template may be assessed (e.g., monitored or tracked) during the future operations of the facial recognition authentication process. 
     Process  700  may begin with storing feature vector(s)  256  from process  250  in a space (e.g., a backup space) in the memory of device  100  in  702 . Feature vector(s)  256  may be stored as temporary template  704 . As feature vector(s)  256  are provided to process  700  after the feature vectors have matching score  260  above unlock threshold  264  (as shown in  FIG. 7 ), temporary template  704  is generated from feature vectors that unlock device  100 . 
     In  706 , the number of temporary templates  704  stored in the memory device are counted and compared to a selected number of temporary templates (e.g., a threshold number of templates). The selected number of temporary templates may include a number of temporary templates suitable for a reasonable set of data (e.g., a number of templates that provides reasonable statistical results). In some embodiments, the selected number of temporary templates is a typical number of templates stored over a selected period of time (e.g., a number of templates typically stored over a number of days or a number of weeks). 
     If the number of temporary templates  704  is below the selected number in  706 , then process  250  is operated again and additional feature vector(s)  256  from successful unlock attempts are added to generate additional temporary templates until the selected number is reached. Once the selected number of temporary templates  704  is reached in  706 , process  700  may continue with selecting a temporary template in  708 . Selecting the temporary template in  708  may include selecting the “virtual” template  710  for process  700 . 
     In certain embodiments, selecting the virtual template in  708  includes assessing temporary templates  704  to determine a median temporary template in the stored temporary templates. The median temporary template may be, for example, a median selected from a cluster of temporary templates  704  in a feature space.  FIG. 9  depicts a representation of an embodiment of feature space  212  with temporary templates  704 . As shown in  FIG. 9 , temporary templates  704  may form into a cluster of data. The cluster of temporary templates  704  may be represented by circle  800 . In some embodiments, outlying temporary templates are not used in determining the cluster of templates (and thus the median template). For example, temporary template  704 ′ may be an outlier temporary template that is not used for determining the cluster of templates. Assessment (e.g., analysis) of the cluster of temporary templates  704  may determine a median temporary template (e.g., temporary template  704 A shown by the star in  FIG. 9 ). The median temporary template  704 A may then be selected as the “virtual” temporary template (e.g., “virtual” template  710  in process  700 , as shown in  FIG. 8 ). 
     In some embodiments, feature space  212  with temporary templates  704  may include multimodal clusters of template data. For example, two or more clusters of data may be present in the feature space with temporary template data. In some embodiments with multimodal clusters of temporary template data, a cluster may be chosen (e.g., a denser cluster or the cluster with more data points) and a median selected from the chosen cluster. In some embodiments with multimodal clusters of temporary template data, it may not be possible to choose a cluster. In such embodiments, the user being captured in the images may be chosen to not be suitable for process  700 . The user being captured in the images may also be chosen to not be suitable for process  700  if no clusters can be determined from the temporary templates data. 
     As shown in  FIG. 8 , after virtual template  710  is selected, the virtual template may be used in facial recognition authentication process  250 ′.  FIG. 10  depicts a flowchart of an embodiment of facial recognition authentication process  250 ′. Process  250 ′ may operate similar to process  250  (shown in  FIG. 7 ) using virtual template  710  instead of template space  220 . The thresholds in process  250 ′ (e.g., unlock threshold  264  and the threshold in  270 ) may be the same as the thresholds in process  250 . In some embodiments, virtual template  710  may also be updated using either first template update process  300  and/or second template update process  400  (described herein with respect to template space  220 ). Thus, virtual template  710  may be updated for changes to the user similar to template space  220 . 
     Process  250 ′ may operate substantially in parallel with process  250  for additional unlock attempts of device  100  after virtual template  710  has been selected. Process  250 ′ and process  250  may operate on the same unlock attempt images  252  captured by device  100 . Process  250 ′ may operate to compare feature vectors  256  from the captured images to virtual template  710  in  258 ′ and determine matching score  260 ′. Matching score  260 ′ may then be used to determine whether device  100  is able to be unlocked using the virtual template. In certain embodiments, process  250 ′ does not determine actual unlocking of device  100  (unlocking of the device is only determined by process  250 ). For example, process  250 ′ is a “virtual” process that assesses the effectiveness of virtual template  710  for facial recognition authentication of the user while process  250  determines unlocking of device  100  using template space  220 . As such, process  250 ′ may be operated to assess the performance of virtual template  710  in comparison to template space  220 . In some embodiments, process  250 ′ may be used to determine unlocking of device  100  in addition to unlocking determine by process  250  (e.g., either process  250  or process  250 ′ may unlock the device). 
     As shown in  FIG. 8 , the performance of virtual template  710  in process  250 ′ is assessed in  712 . Assessing the performance of virtual template  710  in  712  may include assessing any properties that determine the effectiveness or ineffectiveness of using virtual template in attempting to match a user in captured images to the authorized user of device  100 . For example, assessing the performance in  712  may include assessing acceptance rates and/or rejection rates in process  250 ′ using virtual template  710 . The performance of virtual template  710  may be assessed in  712  over a selected time frame (e.g., a period of time) on device  100 . In some embodiments, the performance of virtual template  710  may be assessed for a selected number of unlock attempts. 
     At the same time as the performance of virtual template  710  is being assessed in  712  for the additional unlock attempts, performance of template space  220  (being operated on in process  250 ) may be assessed in  714 . Assessing the performance of template space  220  in  714  may include assessing the same properties assessed in  712  for virtual template  710 . In  716 , the performance of virtual template  710  assessed in  712  may be compared to the performance of template space  220  assessed in  714 . Comparing the performances may include comparing the performances over the selected time frame or the selected number of unlock attempts. 
     In certain embodiments, in  718 , a decision may be made if virtual template  710  is added to template space  220  or if the virtual template is to be deleted. For example, if the performance of virtual template  710  is determined, by comparison  716 , to be less than the performance of static templates  216  in template space  220 , then virtual template  710  may be deleted in  720 . In some embodiments, after virtual template  710  is deleted in  720 , process  700  may operate again beginning with an additional successful unlock attempt to generate a new virtual template for performance assessment. 
     In certain embodiments, if the performance of virtual template  710  is determined, by comparison  716 , to be higher than at least one of static templates  216  in template space  220 , then the virtual template may be added to the template space in  722 . During the enrollment process (e.g., process  200 ), the user is asked to move and behave in certain ways to provide controlled image captures for generating static templates  216 . Controlling the user&#39;s behavior during the image captures for enrollment generates static templates  216  that may provide satisfactory performance of the facial recognition authentication process for the user (e.g., the user encounters acceptable pass/fail rates for unlocking the device). Users may, however, typically operate device  100  with different behavior than the controlled behavior during the enrollment process. As virtual template  710  is generated based on the behavior of the user during unlock attempts of device  100  over a period of time, the virtual template may provide better and more satisfying unlock performance over time as compared to one or more static templates  216 . 
     In some embodiments, adding virtual template  710  to template space  220  in  722  includes adding the virtual template and increasing the number of static templates in the template space (e.g., if there are nine static templates, adding the virtual template creates ten static templates in the template space). In some embodiments, adding virtual template  710  to template space  220  in  722  includes replacing one of static templates  216  in template space  220  with virtual template  710 . For example, virtual template  710  may replace one of static templates  216  that the virtual template outperforms. In some embodiments, virtual template  710  replaces the lowest performing static template  216  in template space  220 . 
     In some embodiments, adding virtual template  710  to template space  220  in  722  includes replacing more than one static template  216  in the template space. For example, virtual template  710  may replace all templates that the virtual template outperforms. In some embodiments, virtual template  710  may replace all static templates  216  in template space  220 . In such embodiments, virtual template  710  becomes the only static template in template space  220 . Virtual template  710  may become the only static template in template space  220  if, for example, the virtual template has a sufficient performance to provide suitable acceptance rates and rejection rates while providing minimal false acceptance and false rejection rates. Virtual template  710  may potentially provide better performance than static templates  216 , which are obtained from enrollment images, because the virtual template is generated from actual use of device  100  by the user (e.g., based on “how” the user uses the device rather than the ideal scenarios used during enrollment). 
     In some implementations, a user may need more than one virtual template to provide suitable performance in replacing all static templates  216  obtained from enrollment. In some implementations, as described above, the performance of virtual template  710  may be determined to be less than the performance of static templates  216  in template space  220  and the virtual template is deleted. The performance of virtual template  710  may be less, for example, if the user has large variances in his/her behavior when attempting to unlock device  100 . In some embodiments, additional mitigations may be applied to template space  220  after virtual template  710  is added to and/or replaces static templates  216  in the template space. Mitigations may be applied, for example, to prevent virtual template  710  from adversely affecting false acceptance and/or false rejection rates. 
       FIG. 11  depicts a flowchart of an embodiment of first template update process  300 . Process  300  may be used to update template space  220  (shown in  FIG. 6 ) with one or more additional dynamic templates  226  based on feature vector(s)  256  from process  250 . In certain embodiments, process  300  is used to update template space  220  for an enrollment profile (e.g., the enrollment profile used in process  250 ) on device  100 . Process  300  may be used to update template space  220  for gradual changes in the appearance of the authorized user associated with the enrollment profile. For example, process  300  may update template space  220  for gradual changes in hair (e.g., hair color, hair length, and/or hair style), weight gain, weight loss, changes in glasses worn, or small disfigurement changes (e.g., black eyes, scars, etc.). Updating template space  220  using process  300  allows the authorized user to continue to successfully access device  100  using facial recognition authentication process  250  despite the gradual changes in the appearance of the user. 
     Process  300  may begin by assessing  302  if matching score  260  is above threshold  304 . Threshold  304  may be a threshold score for determining if feature vector(s)  256  are similar (e.g., close) enough to feature vectors  210  (from static templates  216 ) that feature vector(s)  256  may potentially be used as another template (e.g., the threshold score may determine if feature vectors  256  are within a certain distance of feature vectors  210 ). In certain embodiments, threshold  304  is greater than (above) unlock threshold  264  (e.g., threshold  304  requires a higher matching score than unlock threshold  264 ). Thus, the threshold for feature vector(s)  256  becoming a template may be stricter than the threshold for unlocking the device. Threshold  304  may be set during manufacturing and/or by the firmware of device  100 . Threshold  304  may be updated (e.g., adjusted) by device  100  during operation of the device as described herein. 
     In some embodiments, if matching score  260  is below threshold  304 , then process  300  is stopped and feature vector(s)  256  are deleted from device  100 . In some embodiments, if matching score  260  is below threshold  304 , then process  300  continues with template update sub-process  300 A, described in  FIG. 13 . If matching score  260  is above threshold  304 , then process  300  is continued. In some embodiments, after assessing  302 , one or more qualities in the unlock attempt image are assessed in  306 . For example, pose (e.g., pitch, yaw, and roll of the face), occlusion, attention, field of view, and/or distance in the unlock attempt image may be assessed in  306 . Pose and/or occlusion in the unlock attempt image may be assessed using the landmark and/or occlusion maps described herein. In  308 , if suitable qualifications are not met, then process  300  may be stopped. In certain embodiments, meeting suitable qualifications includes meeting selected criteria in the images for one or more of the assessed qualities described above. For example, selected criteria may include, but not be limited to, the face of the user being in the field of view of the camera, a pose of the user being proper (e.g., the user&#39;s face is not turned to far in any direction from the camera (i.e., the pitch, yaw, and/or roll of the face are not above certain levels) a distance to the face of the user being within a certain distance, the face of the user having occlusion below a minimum value (e.g., the user&#39;s face is not occluded (blocked) more than a minimum amount by another object), the user paying attention to the camera (e.g., eyes of the user looking at the camera), eyes of the user not being closed, and proper lighting (illumination) in the image. In some embodiments, assessing qualities in  306  and  308  may occur in a different location within process  300 . For example, assessing qualities in  306  and  308  may occur after comparing matching score  324  to threshold  326  or after comparing confidence score  332  to confidence score  334  in  336 , described below. 
     If suitable qualifications are met in  308 , then process  300  continues, in  310 , with storing feature vector(s)  256  in a backup space in the memory of device  100 . The backup space in the memory may be, for example, a second space or temporary space in the memory that includes readable/writable memory and/or short term memory. Feature vector(s)  256  may be stored in the memory as temporary template  312 . 
     In certain embodiments, after temporary template  312  is stored in the backup space in the memory, process  300  continues by comparing the temporary template to feature vectors for additional unlock attempt images captured by device  100  for the authorized user. In  314 , additional unlock attempt images are captured of the user (or users if unauthorized access is attempted) as the user(s) during additional (future) unlocking attempts of device  100 . The features of the face of the user in the additional unlock attempt images are encoded in  316  to generate feature vectors  318 . In  320 , feature vectors  318  are compared to temporary template  312  to get matching score  322 . 
     Matching score  322  may then be compared in  324  to threshold  326 . In some embodiments, threshold  326  is unlock threshold  264 . In some embodiments, threshold  326  is threshold  304 . If matching score  322  is above threshold  326  in  324 , then a successful attempt is counted in  328 . If matching score  322  is below threshold  326  in  324 , then an unsuccessful attempt is counted in  330 . Counts  328  and  330  may be continued until a desired number of unlock attempts are made (e.g., a desired number of comparisons of matching score  322  and threshold  326 ). Once the desired number of attempts is made, the number of successful attempts in  328  out of the total number of unlock attempts (e.g., the sum of counts  328  and  330 ) may be used to assess confidence score  332  for temporary template  312 . For example, there may be 45 successful attempts out of 50 total unlock attempts so confidence score  332  is 45/50 or 90%. Confidence score  332  may be used to assess whether or not template  312  is added as dynamic template  226  to template space  220 , shown in  FIG. 6 . 
     As described above, initially after enrollment, the enrollment templates (e.g., static templates  216 , shown in  FIG. 6 ) are added to static portion  222  of template space  220 . After the enrollment process and static templates  216  are added to static portion  222 , process  300 , shown in  FIG. 11 , may be used to add additional templates to template space  220 . Additional templates may be added to dynamic portion  224  as dynamic templates  226  (e.g., a portion from which templates may be added and deleted without a device reset being needed). Dynamic templates  226  may be used in combination with static templates  216  in template space  220  for facial recognition authentication process  250 , shown  FIG. 7 . 
     In certain embodiments, temporary templates  312  generated by process  300 , shown in  FIG. 11 , are added to dynamic portion  224  as dynamic templates  226 , shown in  FIG. 6 , when confidence score  332  for temporary template  312  is higher than a lowest confidence score of static templates  216  in static portion  222 . Confidence score  334  may be equal to a lowest confidence score for static templates  216  in static portion  222  assessed during the same unlock attempts used to assess confidence score  332  for temporary template  312  (e.g., the confidence score for the template with the lowest number of successful unlock attempts during the same unlock attempts using temporary template  312 ). Confidence score  334  may be assessed using the same threshold used for confidence score  332  (e.g., threshold  326 ). 
     In certain embodiments, if, in  336 , confidence score  332  is greater than confidence score  334 , then temporary template  312  is added, in  338 , as dynamic template  226  in dynamic portion  224 . For example, if temporary template  312  has 45 successful unlock attempts out of 50 total unlock attempts while one static template  216  only has 40 successful unlock attempts out of the same 50 total unlock attempts, then temporary template  312  may be added to dynamic portion  224  as one of dynamic templates  226 . If, in  336 , confidence score  332  is less than confidence score  334 , then temporary template  312  is ignored or deleted in  340 . Temporary templates  312  may be added until a maximum number of allowed dynamic templates  226  are stored in dynamic portion  224 . 
     Once dynamic portion  224  reaches its maximum number of dynamic templates  226  in dynamic portion  224 , temporary template  312  may replace one of dynamic templates  226  in  338 . For example, temporary template  312  may replace one of dynamic templates  226  if the temporary template is less of an outlier than one of dynamic templates  226 . In certain embodiments, statistical analysis of the feature vectors that represent dynamic templates  226  and temporary template  312  is used to assess if temporary template  312  is less of an outlier than one of dynamic templates  226 . Statistical analysis may include, for example, classification algorithms operated on feature vectors for the templates. 
       FIG. 12  depicts a representation of an embodiment of template space  220  represented as a feature space. In the feature space depiction of template space  220 , static templates  216 , dynamic templates  226 , and temporary template  312  are represented by feature vectors. For example, static templates  216  are represented by circles, dynamic templates  226  are represented by diamonds, and temporary template  312  is represented by a star. In certain embodiments, as described above, static templates  216  are not allowed to be replaced by temporary template  312 . Thus, if dynamic portion  224  has reached its maximum number of dynamic templates  226 , temporary template  312  may replace one of dynamic templates  226  if temporary template  312  is less of an outlier than one of dynamic templates  226 . 
     Statistical analysis of the feature vectors in the feature space correlating to template space  220  may generate a circle (e.g., circle  342 ) that most closely defines a maximum number of the feature vectors. As shown in  FIG. 12 , circle  342  defines the feature vector for dynamic template  226 ′ as an outlier of the circle. The feature vector for dynamic template  226 ′ is more of an outlier than the feature vector for temporary template  312 . Thus, temporary template  312  may replace dynamic template  226 ′ in template space  220 . If temporary template  312  had been more of an outlier than each of dynamic templates  226 , then the temporary template may not have replaced any one of dynamic templates  226 . 
     In certain embodiments, when temporary template  312  replaces dynamic template  226 ′ in template space  220 , one or more thresholds for device  100  may be recalculated. As temporary template  312  is less of an outlier than dynamic template  226 ′ recalculation of the threshold(s) may further restrict the thresholds (e.g., raise the threshold for matching scores to require closer matching). In some embodiments, the unlock threshold (e.g., unlock threshold  264 , shown in  FIG. 7 ) is made stricter when temporary template  312  replaces dynamic template  226 ′ in template space  220 . In some embodiments, a template update threshold (e.g., threshold  304 , shown in  FIG. 11 ) is made stricter when temporary template  312  replaces dynamic template  226 ′ in template space  220 . 
       FIG. 13  depicts a flowchart of an embodiment of template update sub-process  300 A. As described above, sub-process  300 A may proceed if matching score  260  is below threshold  304  but above unlock threshold  264 . Images with matching scores  260  in such a range (above unlock threshold  264  and below threshold  304 ) may have more uncertainty in matching than images that are above threshold  304  (while still being able to unlock device  100 ). Thus, these more uncertain images may be processed using sub-process  300 A. 
     In sub-process  300 A, one or more qualities in the unlock attempt image are assessed in  350 . Assessing qualities of the unlock attempt image in  350  may be substantially similar to assessing qualities in  306  and  308 , as shown in  FIG. 11 . As shown in  FIG. 13 , if the unlock attempt image passes the assessment of qualities (e.g., meets qualifications) in  350 , then a determination may be made in  352  if there is space (e.g., room) in the backup space used for temporary templates  312  to store another temporary template (e.g., a determination if a maximum number of temporary templates  312  are stored in the backup space). 
     If there is no room in the backup space (“N”), then the unlock attempt image (and its corresponding feature vectors) may be subject to delete policy  354 , as shown in  FIG. 13 . In delete policy  354 , the feature vector(s) in the backup space (e.g., space for temporary templates  312 ) that has selected redundancy (e.g., is most redundant) to the existing features may be replaced in the backup space. 
     If there is room in the backup space (“Y”), then the feature vectors for the unlock attempt image are added to the backup space as a temporary template (e.g., temporary template  312 ) in  356 . Once the temporary template from sub-process  300 A is added to the backup space in  356 , the temporary template may be processed substantially as temporary template  312  (e.g., compared to additional unlock attempt images as shown in  FIG. 11 ). In certain embodiments, the temporary template from sub-process  300 A is used as a template (e.g., temporary template  312  and/or dynamic template  226 ) for a selected amount of time. For example, because the temporary template from sub-process  300 A is originally added with a higher uncertainty than other templates, the amount of time allowed for use of the temporary template from sub-process  300 A may be limited (e.g., the temporary template has a limited lifetime). In some embodiments, the selected amount of time is a maximum amount of successful unlock attempts using the temporary template from sub-process  300 A. 
     As described above, first template update process  300  may be used to update an enrollment profile (e.g., templates in the template space) when device  100  is unlocked or accessed using facial authentication recognition process  250 . First template update process  300  may be used, for example, to update the enrollment profile in response to gradual changes in a user&#39;s appearance (e.g., weight gain/loss). 
     In some embodiments, however, facial features of an authorized user (e.g., the user&#39;s facial appearance) may have changed drastically, or at least to a large enough extent, that the user may encounter difficulty unlocking or accessing features (e.g., operations) on device  100  using facial authentication recognition process  250 , depicted in  FIG. 7 . Drastic or large extent changes in the user&#39;s facial appearance may include, for example, shaving of a beard or mustache, getting a large scar or other disfigurement to the face, making drastic changes in makeup, making drastic hair changes. In some cases, the user may also encounter difficulty in unlocking/accessing device  100  using facial authentication recognition process  250  if there was an error during the enrollment process and/or there are large differences between the user&#39;s environment during the unlock attempt and the time of enrollment. Encountering difficulty in unlocking device  100  using facial authentication recognition process  250  may be a frustrating experience for the user. When difficulty in unlocking device  100  using facial authentication recognition process  250  occurs due to the above described changes/issues, a second template update process (e.g., second template update process  400 , described below) may be used to, at least temporarily, allow the user to unlock/access device using the facial authentication recognition process, despite the issues/changes, after verification of the user&#39;s identity using a second authentication protocol. 
     As shown in  FIG. 7 , the user may attempt a number of unlock attempts unsuccessfully using facial authentication recognition process  250  until the number of unsuccessful unlock attempts reaches the threshold in  270  and device  100  is locked from further attempts to use the facial authentication recognition process. At such time, the user may be presented with one or more options for proceeding with a different type of authentication to unlock or access features on device  100  in unlock options  274 . After the user is successfully authenticated using the selected option, device  100  may, at least temporarily, update the user&#39;s enrollment profile (e.g., using second template update process  400  described below) to allow the user to be able to unlock/access the device in future unlock attempts using facial authentication recognition process  250  despite the changes in the user&#39;s facial appearance that previously prevented the user from using the facial authentication recognition process to unlock/access the device. Thus, the user, by successfully completing authentication using the selected option, may automatically be able to access device  100  using facial authentication recognition process  250  in future unlock attempts for at least a short period of time. 
       FIG. 14  depicts a flowchart of an embodiment of second template update process  400 . Process  400  may be used when facial recognition authentication process  250  is unable to unlock device  100  but the device is unlocked using a passcode or other authentication protocol, as shown in  FIG. 7 . In some embodiments, process  400  may be used when device  100  is unlocked using the passcode immediately after the unlock attempt fails or within a specified time frame after the unlock attempt fails (e.g., in temporal proximity to the unlock attempt). In certain embodiments, process  400  is used to update template space  220  for a single enrollment profile (e.g., the enrollment profile used in process  250 ) on device  100 . If multiple enrollment profiles are on device  100 , process  400  may be operated on only the enrollment profile that is the closest match to the feature vectors from the captured unlock attempt image (as described herein). 
     In certain embodiments, process  400  is used to update template space  220  for the enrollment profile when facial features of the authorized user have changed to an extent that prevents feature vectors generated from an unlock attempt image (e.g., feature vectors  256 ) from being close enough (e.g., within the unlock threshold distance) to static templates  216  and/or dynamic templates  226  to allow device  100  to be unlocked using facial recognition authentication process  250 , shown in  FIG. 7 . For example, process  400  may be used for feature vector  256 B, which is depicted outside circle  265  (the unlock threshold circle) in  FIG. 5 . Possible causes for the user to be able to unlock device  100  using facial recognition authentication process  250  include, but are not limited to, if the authorized user shaves a beard or mustache, gets a large scar or other disfigurement to the face, large changes in makeup, drastic hair change, or has another severe change in a facial feature, these changes may be immediate changes or “step changes” in the facial features of the authorized user that do not allow first template update process  300  to update template space  220  gradually over time. 
     Second template update process  400  may begin by assessing in  402  if matching score  260  is above threshold  404 . Threshold  404  may be a threshold score for determining if feature vector(s)  256  are similar (e.g., close) enough to feature vectors  210  (from static templates  216 ) that feature vector(s)  256  may potentially be used as another template. In certain embodiments, threshold  404  for process  400  is below unlock threshold  264 . Threshold  404  may be below unlock threshold  264  (e.g., more distance allowed between feature vectors and the templates) because the passcode (or other authentication) has been entered prior to beginning process  400 . Thus, the threshold for feature vector(s)  256  becoming a template in process  400  may be less strict than the threshold for unlocking the device and the threshold for process  300 , shown in  FIG. 11 . Threshold  404  may, however, be set at a value that sets a maximum allowable distance between feature vectors  256  for the unlock attempt image and feature vectors for template space  220 . Setting the maximum allowable distance may be used to prevent a user that is not the authorized user but has the passcode for device  100  to be enabled for facial recognition authentication on the device. Threshold  404  may be set during manufacturing and/or by the firmware of device  100 . Threshold  404  may be updated (e.g., adjusted) by device  100  during operation of the device as described herein (e.g., after templates are added or replaced in template space  220 ). 
     Process  404  may be stopped and feature vector(s)  256  are deleted from device  100  if matching score  260  is below threshold  404 . If matching score  260  is above threshold  404 , then process  400  is continued. In some embodiments, after assessing  404 , one or more qualities in the unlock attempt image are assessed in  406 . For example, pose (e.g., pitch, yaw, and roll of the face), occlusion, attention, field of view, and/or distance in the unlock attempt image may be assessed in  406 . In some embodiments, pose and/or occlusion in the unlock attempt image are assessed using the landmark and/or occlusion maps described herein. In  408 , if suitable qualifications (as described above) are not met, then process  400  may be stopped. 
     If suitable qualifications are met in  408 , then process  400  continues in  410  with storing feature vector(s)  256  in a backup space in the memory of device  100 . The backup space in the memory for process  400  may be a different backup space than used for process  300 . For example, the backup space in the memory for process  400  may be a temporary space in the memory that includes readable/writable memory partitioned from backup space used for process  300 . Feature vector(s)  256  may be stored in the memory as temporary template  412 . 
     In certain embodiments, after temporary template  412  is stored in the backup space, temporary template  412  may be compared to feature vectors for additional images from failed facial recognition authentication unlock attempts of device  100 . For example, in process  400  additional unlock failed attempt images may be captured in  414 . If the correct passcode is entered in  416 , then feature vectors for the images captured in  414  may be encoded in  418  to generate feature vectors  420 . 
     In certain embodiments, in  422 , feature vectors  420  are compared to the feature vector(s) for temporary template  412 . Comparison of feature vectors  420  and the feature vector(s) for temporary template  412  may provide matching score  424 . Matching score  424  may be compared in  426  to threshold  428 . Threshold  428  may be, for example, a similarity threshold or a threshold that defines at least a minimum level of matching between the feature vector(s) for temporary template  412  and feature vectors  420  obtained from the additional images from failed facial recognition authentication attempts that are followed by entering of the passcode for device  100 . Thus, threshold  428  may be set at a value that ensures at least a minimum amount of probability that the change in the user&#39;s features that caused the failed unlock attempt and generated temporary template  412  is still present in the images from additional failed unlock attempts using facial recognition authentication. 
     If matching score  424  is above threshold  428  in  426 , then a successful match is counted in  430 . If matching score  424  is below threshold  428  in  426 , then an unsuccessful match is counted in  432 . Counts  430  and  432  may be continued until a desired number of failed unlock attempts are made using facial recognition authentication (e.g., a desired number of comparisons of matching score  424  and threshold  428 ). Once the desired number of attempts is made, the number of successful matches in  430  out of the total number of failed unlock attempts (e.g., the sum of counts  430  and  432 ) may be used to assess confidence score  434  for temporary template  412 . For example, there may be 18 successful matches (e.g., comparisons) of matching score  424  and threshold  428  out of 20 total failed unlock attempts. Confidence score  434  may be used to assess whether or not template  412  is added as dynamic template  226  to template space  220  for the enrollment profile, shown in  FIG. 6 . 
     In some embodiments, it may be assumed that if a step change occurs in the facial features of the authorized user, the step change may remain for a number of successive unlock attempts using facial recognition authentication. For example, if the user shaved a beard, then the step change should remain for at least some length of time (e.g., at least a week). In such embodiments, if a successful unlock attempt (or a desired number of successful unlock attempts) using facial recognition authentication occurs before a selected number of successive unlock attempts is reached (e.g.,  10  or  15  unlock attempts), then temporary template  412  may be deleted from the backup space in the memory. In some embodiments, the assumption that the step change may remain for a number of successive unlock attempts may not apply (e.g., if the user&#39;s step change was due to temporary application of makeup). 
     In certain embodiments, in  436 , confidence score  434  is compared against threshold  438  to assess if the confidence score is greater than the threshold. Threshold  438  may be a threshold selected to ensure a minimum number of successful comparisons of matching score  424  and threshold  428  are reached before allowing template  412  to be added to template space  220 . In  436 , if confidence score  434  is greater than threshold  438 , then, in  440 , temporary template  412  may be added to template space  220  or temporary template  412  may replace a template in the template space  220  (e.g., replace one of dynamic templates  226 ). If confidence score  434  is less than threshold  438 , then temporary template  412  may be ignored or deleted in  442 . 
     As described above, temporary template  412  generated by process  400  may be added to dynamic portion  224  of template space  220  for the enrollment profile as one of dynamic templates  226 , shown in  FIG. 6 . For process  400 , shown in  FIG. 14 , the passcode (or other authentication) has been used to verify that temporary template  412  is for the authorized user. Thus, in certain embodiments, temporary template  412  is added to template space  220  in  440  without a need for comparison to dynamic templates  226  already in dynamic portion  224 . If the maximum number of allowed dynamic templates  226  in dynamic portion  224  has not been reached, then temporary template  412  is added to the dynamic portion as one of dynamic templates  226 . 
     If the maximum number of allowed dynamic templates  226  in dynamic portion  224  has been reached, then temporary template  412  may replace one of dynamic templates  226  in the dynamic portion. As the passcode (or other authentication) has been used to verify temporary template  412  is for the authorized user, the temporary template may replace one of dynamic templates  226  in dynamic portion  224  even if the temporary template is more of an outlier than each of dynamic templates  226 . In certain embodiments, temporary template  412  replaces the largest outlier of dynamic templates  226  regardless of the relative lie (e.g., outlie) of the temporary template. In some embodiments, temporary template  412  may replace a dynamic template that is redundant (e.g., most redundant) to the existing dynamic templates even if the temporary template is more of an outlier than each of the dynamic templates. 
       FIG. 15  depicts a representation of an embodiment of template space  220  represented as a feature space with a feature vector for temporary template  412 . In the feature space depiction of template space  220  in  FIG. 15 , static templates  216 , dynamic templates  226 , and temporary template  412  are represented by feature vectors. Static templates  216  are represented by circles, dynamic templates  226  are represented by diamonds, and temporary template  412  is represented by a star. As described above, static templates  216  may not be replaced by temporary template  412 . Thus, if dynamic portion  224  has reached its maximum number of dynamic templates  226 , temporary template  412  may replace one of dynamic templates  226 . 
     Statistical analysis of the feature vectors in the feature space correlating to template space  220  may generate a circle (e.g., circle  444 ) that most closely defines a maximum number of the feature vectors. As shown in  FIG. 15 , the feature vector for dynamic template  226 ′ is the largest outlier of each of the feature vectors for dynamic templates  226 . Thus, temporary template  412  may replace dynamic template  226 ′ in template space  220  regardless of the position of the feature vector for the temporary template. In the example depicted in  FIG. 15 , the addition of the feature vector for temporary template  412  shifts circle  444  towards the feature vector for temporary template  412  and may cause the feature vector for dynamic template  226 ′ to become the largest outlier of the circle. In some embodiments, when temporary template  412  replaces dynamic template  226 ′ in template space  220 , one or more thresholds for device  100  may be recalculated. 
     In some embodiments, a temporary template (e.g., either temporary template  312  or temporary template  412 ) may be used to unlock device  100  for a selected period of time while the temporary template is in the backup space of the memory (e.g., before the temporary template is added to template space  220 ). The temporary template may be used to unlock device  100  after the passcode (or other user authentication protocol) is used in combination with the temporary template. For example, for temporary template  412 , the passcode has been entered to unlock device  100  before temporary template  412  is generated and stored in the backup space of the device memory. Temporary template  412  may then be used to allow unlocking of device  100  using facial recognition authentication for a selected time period (e.g., a few days or a week). After the selected time period expires, if temporary template  412  has not been added to template space  220 , the user may be prompted for the passcode if facial recognition authentication of the user fails. 
     In certain embodiments, one or more process steps described herein may be performed by one or more processors (e.g., a computer processor) executing instructions stored on a non-transitory computer-readable medium. For example, process  200 , process  250 , process  300 , process  400 , and process  700 , shown in  FIGS. 4, 7, 8, 11, and 14 , may have one or more steps performed by one or more processors executing instructions stored as program instructions in a computer readable storage medium (e.g., a non-transitory computer readable storage medium). 
       FIG. 16  depicts a block diagram of one embodiment of exemplary computer system  510 . Exemplary computer system  510  may be used to implement one or more embodiments described herein. In some embodiments, computer system  510  is operable by a user to implement one or more embodiments described herein such as process  200 , process  250 , process  300 , and process  400 , and process  700 , shown in  FIGS. 4, 7, 8, 11, and 14 . In the embodiment of  FIG. 16 , computer system  510  includes processor  512 , memory  514 , and various peripheral devices  516 . Processor  512  is coupled to memory  514  and peripheral devices  516 . Processor  512  is configured to execute instructions, including the instructions for process  200 , process  250 , process  300 , process  400 , and/or process  700 , which may be in software. In various embodiments, processor  512  may implement any desired instruction set (e.g. Intel Architecture-32 (IA-32, also known as x86), IA-32 with 64 bit extensions, x86-64, PowerPC, Sparc, MIPS, ARM, IA-64, etc.). In some embodiments, computer system  510  may include more than one processor. Moreover, processor  512  may include one or more processors or one or more processor cores. 
     Processor  512  may be coupled to memory  514  and peripheral devices  516  in any desired fashion. For example, in some embodiments, processor  512  may be coupled to memory  514  and/or peripheral devices  516  via various interconnect. Alternatively or in addition, one or more bridge chips may be used to coupled processor  512 , memory  514 , and peripheral devices  516 . 
     Memory  514  may comprise any type of memory system. For example, memory  514  may comprise DRAM, and more particularly double data rate (DDR) SDRAM, RDRAM, etc. A memory controller may be included to interface to memory  514 , and/or processor  512  may include a memory controller. Memory  514  may store the instructions to be executed by processor  512  during use, data to be operated upon by the processor during use, etc. 
     Peripheral devices  516  may represent any sort of hardware devices that may be included in computer system  510  or coupled thereto (e.g., storage devices, optionally including computer accessible storage medium  600 , shown in  FIG. 17 , other input/output (I/O) devices such as video hardware, audio hardware, user interface devices, networking hardware, etc.). 
     Turning now to  FIG. 17 , a block diagram of one embodiment of computer accessible storage medium  600  including one or more data structures representative of device  100  (depicted in  FIG. 1 ) included in an integrated circuit design and one or more code sequences representative of process  200 , process  250 , process  300 , process  400 , and/or process  700  (shown in  FIGS. 4, 7, 8, 11, and 14 ). Each code sequence may include one or more instructions, which when executed by a processor in a computer, implement the operations described for the corresponding code sequence. Generally speaking, a computer accessible storage medium may include any storage media accessible by a computer during use to provide instructions and/or data to the computer. For example, a computer accessible storage medium may include non-transitory storage media such as magnetic or optical media, e.g., disk (fixed or removable), tape, CD-ROM, DVD-ROM, CD-R, CD-RW, DVD-R, DVD-RW, or Blu-Ray. Storage media may further include volatile or non-volatile memory media such as RAM (e.g. synchronous dynamic RAM (SDRAM), Rambus DRAM (RDRAM), static RAM (SRAM), etc.), ROM, or Flash memory. The storage media may be physically included within the computer to which the storage media provides instructions/data. Alternatively, the storage media may be connected to the computer. For example, the storage media may be connected to the computer over a network or wireless link, such as network attached storage. The storage media may be connected through a peripheral interface such as the Universal Serial Bus (USB). Generally, computer accessible storage medium  600  may store data in a non-transitory manner, where non-transitory in this context may refer to not transmitting the instructions/data on a signal. For example, non-transitory storage may be volatile (and may lose the stored instructions/data in response to a power down) or non-volatile. 
     As described herein, one aspect of the present technology is the gathering and use of data available from specific and legitimate sources to improve the delivery to users of invitational content or any other content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to identify a specific person. Such personal information data can include demographic data, location-based data, online identifiers, telephone numbers, email addresses, home addresses, data or records relating to a user&#39;s health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other personal information. For image data, the personal information data may only include data from the images of the user and not the images themselves. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to control unlocking and/or authorizing devices using facial recognition. Accordingly, use of such personal information data enables calculated control of access to devices. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. 
     The present disclosure contemplates that those entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities would be expected to implement and consistently apply privacy practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. Such information regarding the use of personal data should be prominent and easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate uses only. Further, such collection/sharing should occur only after receiving the consent of the users or other legitimate basis specified in applicable law. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations that may serve to impose a higher standard. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, such as in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing identifiers, controlling the amount or specificity of data stored (e.g., collecting location data at city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods such as differential privacy. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users based on aggregated non-personal information data or a bare minimum amount of personal information, such as the content being handled only on the user&#39;s device or other non-personal information available to the content delivery services. 
     Further modifications and alternative embodiments of various aspects of the embodiments described in this disclosure will be apparent to those skilled in the art in view of this description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the general manner of carrying out the embodiments. It is to be understood that the forms of the embodiments shown and described herein are to be taken as the presently preferred embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed, and certain features of the embodiments may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description. Changes may be made in the elements described herein without departing from the spirit and scope of the following claims.

Metadata:
Filing Date: 20190531
Publication Date: 20210907
Grant Date: 20210907
Priority Date: 20180603
Inventors: MOSTAFA, Eslam A.
HO, KELSEY Y.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06V40/172", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V10/762", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/168", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F21/32", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F18/23", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V10/762", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/50", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/168", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/166", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/172", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F21/32", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F21/32", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06K9/00268", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06K9/00255", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 77559067