Detection of identity changes during facial recognition enrollment process

A device with a camera may utilize an enrollment process to capture images of an authorized user to enroll the user for a facial recognition authorization process. The enrollment process may include one or more processes that identify if an identity of the authorized user (e.g., the subject of the enrollment process) has switched during the enrollment process. The processes may include detection and verification of the switch in identities by comparing features of subjects in images as the images are captured during the enrollment process. If the identity of the subject is determined to be switched from the authorized user during the enrollment process, the enrollment process may be restarted. Additionally, clustering of feature vectors from the enrollment images may be used to remove outlying feature vectors that may be generated from one or more images of a subject other than the authorized user.

BACKGROUND

1. Technical Field

Embodiments described herein relate to methods and systems for face detection in images capture by a camera on a 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 enrollment process may be used to obtain enrollment (e.g., reference or template) images of an authorized user of the device. During a typical enrollment process, multiple enrollment images are taken of the authorized user. While taking the multiple enrollment images, it is possible that multiple users (e.g., multiple subjects) are captured in the enrollment images. For example, multiple subjects may be involved in enrollment if the primary (authorized) user seeks assistance from an additional person (e.g., a salesperson or technical assistance person) during the enrollment process. In such cases, the device may inadvertently capture enrollment images of the additional person during the enrollment process. If the enrollment images with the additional person are used to create a facial recognition profile for the device along with enrollment images of the primary user, the accuracy of facial recognition authentication for the primary user (e.g., the intended subject of the profile) may be reduced.

SUMMARY

In certain embodiments, during an enrollment process (e.g., a facial recognition enrollment authorization process), a device may capture a first set of images of a face of a first subject (e.g., a user trying to be authorized for facial recognition authorization by the device). For example, the first set of images of the face of the first subject may be captured while the face of the first subject is in a selected pose and/or has selected facial feature movements (e.g., mouth shape, eye look, etc.). While the enrollment process continues, one or more additional images of a subject using the device (e.g., the first subject and/or an additional subject) may be captured by the device. For example, additional images (e.g., a second set of images) may be captured when the device prompts the user to capture additional images for different poses and/or different facial feature movements (e.g., different mouth shape, different eye looks, etc.) of the user.

In certain embodiments, as the images in the second set of images are captured (e.g., as second images are captured), the subject in the second images may be compared to the subject in the first set of images (e.g., the first images of the first subject). In certain embodiments, the subjects are compared by comparing feature vectors generated by encoding facial features in the images in a feature space. The subject in the second images may be compared over time (e.g., by comparing images as the images are captured) to the subject in the first images (e.g., the first subject). If the subject in the second images is consistently determined to be a different subject than the subject in the first images (e.g., a selected number of consecutive second images are determined to be a different subject), then the enrollment process may be stopped and a prompt or other command may be given to start the enrollment process over (e.g., restart the enrollment process).

In some embodiment, the feature vectors for both the first images and the second images are combined in the feature space. Clustering (e.g., classification) of the feature vectors in the feature space may be used to remove outliers from the enrollment signature. Outliers in the feature space (e.g., outlying feature vectors in the feature space) may be more likely to be due to different users in the images than due to different poses of the same user (e.g., the primary user or first subject).

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.

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.

As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve the operation and access to devices. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include image data (e.g., data from images of the user), demographic data, location-based data, telephone numbers, email addresses, home addresses, or any other identifying 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 further contemplates that the 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 should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, in the case of unlocking and/or authorizing devices using facial recognition, personal information from users should be collected for legitimate and reasonable uses of the entity, as such uses pertain only to operation of the devices, and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the user and the personal information data should remain secured on the device on which the personal information is collected. Additionally, such entities would take 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.

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, 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.

FIG. 1depicts a representation of an embodiment of a device including a camera. In certain embodiments, device100includes camera102, processor104, memory106, and display108. Device100may 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, device100is 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, device100includes any device used by a user with processor104, memory106, and display108. Display108may be, for example, an LCD screen or touchscreen. In some embodiments, display108includes a user input interface for device100(e.g., the display allows interactive input for the user).

Camera102may be used to capture images of the external environment of device100. In certain embodiments, camera102is positioned to capture images in front of display108. Camera102may be positioned to capture images of the user (e.g., the user's face) while the user interacts with display108.FIG. 2depicts a representation of an embodiment of camera102. In certain embodiments, camera102includes one or more lenses and one or more image sensors103for capturing digital images. Digital images captured by camera102may include, for example, still images, video images, and/or frame-by-frame images.

In certain embodiments, camera102includes image sensor103. Image sensor103may 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, camera102includes more than one image sensor to capture multiple types of images. For example, camera102may include both IR sensors and RGB (red, green, and blue) sensors. In certain embodiments, camera102includes illuminators105for illuminating surfaces (or subjects) with the different types of light detected by image sensor103. For example, camera102may 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 speckle pattern projector). In some embodiments, the flood IR source and speckle pattern projector are other wavelengths of light (e.g., not infrared). In certain embodiments, illuminators105include an array of light sources such as, but not limited to, VCSELs (vertical-cavity surface-emitting lasers). In some embodiments, image sensors103and illuminators105are included in a single chip package. In some embodiments, image sensors103and illuminators105are located on separate chip packages.

In certain embodiments, image sensor103is 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 sensor103(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, illuminator105A may provide flood IR illumination to flood the subject with IR illumination (e.g., an IR flashlight) and image sensor103may 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. For depth detection or generating a depth map image, illuminator105B may provide IR illumination with a speckle pattern. The speckle pattern may be a pattern of light spots (e.g., a pattern of dots) with a known, and controllable, configuration and pattern projected onto a subject. Illuminator105B may include a VCSEL array configured to form the speckle pattern or a light source and patterned transparency configured to form the speckle pattern. The configuration and pattern of the speckle pattern provided by illuminator105B may be selected, for example, based on a desired speckle pattern density (e.g., dot density) at the subject. Image sensor103may capture images of the subject illuminated by the speckle pattern. The captured image of the speckle 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 camera102include images with the user's face (e.g., the user's face is included in the images). An image with the user's face may include any digital image with at least some portion of the user's face shown within the frame of the image. Such an image may include just the user's face or may include the user's face in a smaller part or portion of the image. The user's face may be captured with sufficient resolution in the image to allow image processing of one or more features of the user's face in the image.

Images captured by camera102may be processed by processor104.FIG. 3depicts a representation of an embodiment of processor104included in device100. Processor104may include circuitry configured to execute instructions defined in an instruction set architecture implemented by the processor. Processor104may execute the main control software of device100, such as an operating system. Generally, software executed by processor104during use may control the other components of device100to 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, processor104includes image signal processor (ISP)110. ISP110may include circuitry suitable for processing images (e.g., image signal processing circuitry) received from camera102. ISP110may include any hardware and/or software (e.g., program instructions) capable of processing or analyzing images captured by camera102.

In certain embodiments, processor104includes secure enclave processor (SEP)112. In some embodiments, SEP112is involved in a facial recognition authentication process involving images captured by camera102and processed by ISP110. SEP112may be a secure circuit configured to authenticate an active user (e.g., the user that is currently using device100) as authorized to use device100. 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., memory106) 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, SEP112may 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 camera102and comparing the captured images to previously collected images of an authorized user for device100. In some embodiments, the functions of ISP110and SEP112may be performed by a single processor (e.g., either ISP110or SEP112may perform both functionalities and the other processor may be omitted).

In certain embodiments, processor104performs an enrollment process (e.g., image enrollment process200, as shown inFIG. 4, or a registration process) to capture images (e.g., the previously collected images) for an authorized user of device100. During the enrollment process, camera module102may capture (e.g., collect) images and/or image data from an authorized user in order to permit SEP112(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 device100. The templates may be stored, for example, in a template space in memory106of device100. 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 may be performed by processor104to 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'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 module102captures 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 depth map image). In certain embodiments, ISP110and/or SEP112process the flood IR images and depth map images independently of each other before a final authentication decision is made for the user. For example, ISP110may process the images independently to determine characteristics of each image separately. SEP112may 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 depth map images) may be combined to make a decision on the identity of the user and, if authenticated, allow the user to use device100(e.g., unlock the device).

In some embodiments, ISP110and/or SEP112combine the images in each pair to provide a composite image that is used for facial recognition. In some embodiments, ISP110processes the composite image to determine characteristics of the image, which SEP112may compare with the stored templates to make a decision on the identity of the user and, if authenticated, allow the user to use device100.

In some embodiments, the combination of flood IR image data and depth map image data may allow for SEP112to compare faces in a three-dimensional space. In some embodiments, camera module102communicates image data to SEP112via 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 module102and/or ISP110may perform various processing operations on image data before supplying the image data to SEP112in order to facilitate the comparison performed by the SEP.

In certain embodiments, processor104operates 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 processor104and/or on device100. In some embodiments, one or more neural network modules114are used to operate the machine learning models on device100. Neural network modules114may be located in ISP110and/or SEP112.

Neural network module114may include any combination of hardware and/or software (e.g., program instructions) located in processor104and/or on device100. In some embodiments, neural network module114is 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 module114is 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 module114may 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 module114installed on device100. Providing the operating parameters generated from training to neural network module114on device100allows 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. 4depicts a flowchart of an embodiment of image enrollment process200for an authorized user of device100. Process200may be used to create an enrollment profile for an authorized user of device100that is stored in the device (e.g., in a memory coupled to SEP112). The enrollment profile may include one or more templates for the authorized user created using process200. In some embodiments, process200is used to create two templates for the authorized user. A first template may be a full face template (e.g., a template used with the full face facial recognition authentication process) and the second template may be a partial face template (e.g., a template used with the partial face facial recognition authentication process). The enrollment profile and the templates associated with the enrollment profile may be used in a facial recognition authentication 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, process200is used when device100is 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, process200may be initiated when device100is 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, process200may be initiated by the authorized user when the user desires to enroll in a facial recognition process, update security settings for device100, re-enroll, and/or add an enrollment profile on the device.

In certain embodiments, process200begins with authenticating the user in202. In202, the user may be authenticated on device100using 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 in202, one or more enrollment (e.g., reference or registration) images of the user are captured in204. The enrollment images may include images of the user illuminated by flood illuminator105A (e.g., flood IR images) and/or images of the user illuminated by speckle illuminator105B (e.g., depth map images). As described herein, flood IR images and depth map images may be used independently and/or in combination in facial recognition processes on device100(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 camera102as the user interacts with device100. For example, the enrollment images may be captured as the user follows prompts on display108of device100. The prompts may include instructions for the user to make different motions and/or poses while the enrollment images are being captured. During204, camera102may 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 camera102on device100.

After the multiple enrollment images are captured in204, selection of enrollment images for further image processing may be made in206. Selection of enrollment images206, and further processing of the images, may be performed by ISP110and/or SEP112. 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 in206may 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's face being proper (e.g., the user'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 camera102and 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'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 in206, features of the user in the selected (template) images may be encoded in208. 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 vectors210may be the output of the encoding in208. 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. 5depicts a representation of an embodiment of feature space212with feature vectors210. Each feature vector210(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 vectors210are generated from a single user'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 circle211inFIG. 5.

As shown inFIG. 4, process200may include, in214, storing feature vectors210in a memory of device100(e.g., a memory protected by SEP112). In certain embodiments, feature vectors210are stored as static templates216(e.g., enrollment templates or reference templates) in a template space of the memory (e.g., template space220described below). Static templates216may be used for the enrollment profile created by process200. In some embodiments, static templates216(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 depth map images. It is to be understood that the separate templates obtained from flood IR images and depth map images may be used independently and/or in combination during additional processes described herein. For simplicity in this disclosure, static templates216are described generically and it should be understood that static templates216(and the use of the templates) may refer to either templates obtained from flood IR images or templates obtained from depth map images. In some embodiments, a combination of the flood IR images and depth map images may be used to generate templates. For example, pairs of feature vectors obtained from flood IR images and depth map images may be stored in static templates216to be used in one or more facial recognition processes on device100.

FIG. 6depicts a representation of an embodiment of template space220for an enrollment profile in memory106of device100. In certain embodiments, template space220is located in a portion of memory106of device100protected by SEP112. In some embodiments, template space220includes static portion222and dynamic portion224. Static templates216may be, for example, added to static portion222of template space220(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 portion222includes a certain number of static templates216. For example, for the embodiment of template space220depicted inFIG. 6, six static templates216are allowed in static portion222. In some embodiments, nine static templates216may be allowed in static portion222. Other numbers of static templates216in static portion222may also be contemplated. After the enrollment process for the enrollment profile is completed and static templates216are added to static portion222, additional dynamic templates226may be added to dynamic portion224of template space220for the enrollment profile (e.g., a portion from which templates may be added and deleted without a device reset being needed).

Static templates216may thus be enrollment templates (or reference templates) generated by enrollment process200for the enrollment profile associated with the enrollment process. After enrollment process200is completed, a selected number of static templates216are stored in static portion222of template space220for the enrollment profile. The number of static templates216stored in static portion222after enrollment process200may 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 process200, static templates216include feature vectors210(e.g., the enrollment or reference feature vectors) that can be used for facial recognition of the authorized user associated with the enrollment profile.

In some embodiments, template space220may be used in a facial recognition authentication process. The facial recognition authentication process may be used to authenticate a user as an authorized user of device100using facial recognition of the user. During the facial recognition authentication process, images captured of the user during an unlock attempt may be compared to the images in template space220(e.g., by comparing feature vectors of the images) to determine if the user attempting to unlock the device is an authorized user of the device. Authentication of the authorized user may allow the user to access and use device100(e.g., unlock the device).

In certain embodiments, some of the images captured during204in image enrollment process200, as shown inFIG. 4, are processed using identity switch detection process300and identity switch verification process400before continuing with the image enrollment process (e.g., before proceeding to selection of enrollment images in206). Identity switch detection process300and identity switch verification process400may be used to detect and verify if the identity of a subject has switched during image enrollment process200. In some embodiments, process300and process400are initiated after at least one static template216has been generated (e.g., after at least one image of the subject has been added to the template space).

FIG. 7depicts a representation of an embodiment of identity switch detection process300. Process300may be used to determine (e.g., detect) if an identity of a subject (e.g., user) has changed (e.g., switched) between one or more images (or one or more sets of images) captured during204in enrollment process200(shown inFIG. 4). Process300may begin with assessing a first matching score in302. The first matching score may be assessed between first captured image301A and second captured image301B in302. First captured image301A may be an image that has been processed using process200to generate static template216(e.g., an enrollment image selected in206, as shown inFIG. 4). In certain embodiments, second captured image310B used in process300to assess the first matching score in302is the image captured subsequent to first captured image301A (e.g., the second captured image and the first captured image are consecutively captured images with the second captured image being taken after static template216is generated using the first captured image).

As described herein, a “matching score” may be determined by encoding features (e.g., facial features) in different images (e.g., first captured image301A and second captured image301B) to generate feature vectors and comparing the generated feature vectors. Comparison of the feature vectors of the different images may provide a matching score that provides an assessment of similarities between the different images. The closer (e.g., the less distance or less differences) that the feature vectors in a first image and the feature vectors in a second image are, the higher the matching score between the first and second images. Thus, the higher the assessed matching score is, the more similar the subjects in the different images.

As described herein for process300, “first captured image” or “second captured image” may be a single captured image or a set of captured images (e.g., a set of multiple captured images). For example, in one embodiment, “first captured image” may be a set of multiple images taken (captured) while a face of a subject is in a selected pose and/or has selected movements during the enrollment process. The multiple images may be taken/captured in rapid succession while the subject is illuminated with a specific illumination (e.g., either flood IR illumination or speckle pattern illumination) to substantially capture “a moment” in time. For example, the multiple images may be 60 frames captured in a second (e.g., 60 fps) while being illuminated with the flood IR illumination. In such an embodiment, the “first captured image” may be a composite image of the multiple images in the set of images taken while being illuminated with the specific illumination. For the composite image, the feature vectors for images in the set of images may be combined to provide a set of feature vectors that describe the “first captured image” in a feature space. Similarly, the “second captured image” may be a composite image of a second set of multiple images while being illuminated with the specific illumination. In some embodiments, the “second captured image” is taken with the face of the subject in a selected pose and/or with selected movements that are different than the pose and/or movements of the “first captured image”.

For process300, the first matching score assessed in302may be compared to a first threshold in304. If the first matching score is above the first threshold (“N” inFIG. 7), then the identity of the subject in second captured image301B may be assumed to be the same as the subject in first captured image301A and enrollment process200, shown inFIG. 4, may be continued by either capturing additional enrollment images in204(which may also be subject to assessment under process300) or continuing the next steps of the enrollment process (e.g., continuing the steps in process200to generate static templates216). If the first matching score is below the first threshold (“Y” inFIG. 7), then a second matching score may be assessed in306.

In certain embodiments, the second matching score assessed in306is a matching score between feature vectors in second captured image301B and reference signature307(e.g., a set of reference feature vectors) for previously captured images. Reference signature307may be, for example, a set of feature vectors generated by averaging the feature vectors for all the images captured prior to capturing the second captured image during the enrollment process. In308, the second matching score may be compared to a second threshold. If the second matching score is above the second threshold (“N”), then the identity of the subject in the second captured image may be assumed to be substantially similar to the subject in the reference signature and enrollment process200, shown inFIG. 4, may be continued. If the second matching score is below the second threshold (“Y”), then the identity of the subject in the second captured image may be assumed to be a different person than the subject in the reference signature (e.g., a person that does not resemble the subject in the reference signature). Thus, when the second matching score is below the second threshold (“Y”), the second captured image may be added, in310to temporary template312. Temporary template312may be provided to identity switch verification process400(shown inFIG. 8) for additional images taken/captured after the second captured image. Adding the second captured image to temporary template312may include adding the feature vectors for the second captured image to the temporary template. By adding the feature vectors for the second captured image to temporary template312, the subject in the second captured image is provided as the subject in the temporary template

FIG. 8depicts a representation of an embodiment of identity switch verification process400. Process400may be used to verify during the capture of additional images if the subject placed in temporary template312(e.g., the subject in second captured image301B that has been identified as having different features by process300) is a subject with a different identity from the subject enrolled by image enrollment process200. One or more comparisons using the additional images captured may be used to verify if the subject in temporary template312is a different subject than the subject enrolled by image enrollment process200or if there was an error or other factor in identifying the subject in second captured image301B as being a different subject than the subject enrolled by image enrollment process200.

Process400may begin (e.g., be triggered) after the feature vectors for the second captured image are added to temporary template312in process300, as described above. Process400may operate on additional images captured401. Additional images capture401may include images after second captured image301B as each additional image is captured. Process400may be used to verify if a change of identity has occurred by comparing the identity of the subject in each additional image captured after the second captured image with the identity of the subject in temporary template312(e.g., the identity of the subject in second captured image301B).

Process400may begin by assessing a matching score (e.g., a third matching score) between feature vectors for additional image captured401and feature vectors in temporary template312in402. In404, the third matching score may be compared to another threshold (e.g., a third threshold). If the third matching score is less than the third threshold (“N”), then a “false alarm” may be determined in406. The false alarm may be an indication that additional image captured401appears to have a different subject than the second subject in temporary template312as assessed by the low third matching score (e.g., below the third threshold). In some cases, this may be an indication that there was not an identity switch (e.g., change in the subject). In some cases, there may still be an identity switch. Since the subject in additional image captured401, however, does not match the subject in temporary template312, when the false alarm is determined, processes300and400may be reset (e.g., the temporary template will be cleared and process300may begin again with the next captured image and, potentially, process400may begin again).

If the third matching score is higher than the third threshold (“Y”) in404, then another matching score (e.g., a fourth matching score) may be assessed in408. The fourth matching score may be a matching score between the additional image captured and the image captured immediately prior to the additional image (e.g., preceding image409). In410, the fourth matching score may be compared to yet another threshold (e.g., a fourth threshold). In some embodiments, the fourth threshold is substantially the same as the third threshold. If the fourth matching score is less than the fourth threshold (“N”), then the “false alarm” may be determined in412.

If the fourth matching score is higher than the fourth threshold (“Y”) in410, then additional captured image401may be added into temporary template312(e.g., the feature vectors for the additional captured image are added to the feature vectors in the temporary template). In416, the number of images (frames) in temporary template312may be counted and compared to a predetermined number of images. The predetermined number of images may be a number of images selected to provide a reasonable assessment that the identity of a subject has changed. In some embodiments, the predetermined number of images may also be selected based on properties of the device and/or the camera used in capturing images (e.g., a frame capture speed of the camera).

In416, if the number of images in temporary template312is less than the selected number of images (“N”), then process400may continue at402with additional captured images401. In certain embodiments, if the “false alarm” is determined in process400at any point in time before the selected number of images is reached in416, the false alarm determination will reset process300and process400(e.g., temporary template312will be cleared and the processes will begin again).

If the number of images in temporary template312is equal to or greater than the selected number of images (“Y”), then process400may continue with assessing a distance between facial signatures (e.g., feature vectors) for the first subject (e.g., the subject in static template216, shown inFIG. 4, generated by image enrollment process200) and the subject in temporary template312in418. The distance may be assessed by assessing distance between feature vectors for the first subject and the subject in temporary template312. For example, the distance may be assessed by assessing an average distance between feature vectors for the first subject and the subject in temporary template312.

In420, the assessed distance may be compared to a threshold for the distance between the facial signatures for the first subject and the subject in temporary template312. If the distance between the facial signatures is above the distance threshold (“Y”), then an identity switch between the subjects may be confirmed in422. The identity switch may confirm that the identity of the first subject is different than the identity of the subject in temporary template312. In certain embodiments, if the identity switch is confirmed in422, image enrollment process200may be started over (e.g., static templates216and images are cleared from memory and the process is restarted). In some embodiments, when the identity switch is confirmed in422, a notice or other prompt may be given to the user of the device that image enrollment process200is going to start over. For example, a user interface (UI) of the device may provide notification that the image enrollment process encountered an error and needs to be started over.

If the distance between the facial signatures is below the distance threshold (“N”), then process300and process400may be restarted. In some embodiments, if the distance between the facial signatures is below the distance threshold, temporary template312may be cleared (e.g., cleared from memory) and the feature vectors in the temporary template are no longer used. In some embodiments, if the distance between the facial signatures is below the distance threshold, temporary template312may be added to static template216for image enrollment process200. A decision on whether temporary template312is cleared from memory or added to the template may involve comparison of the distance between the facial signatures to another threshold that is lower than the threshold used in420.

In some embodiments, thresholds used in process300and/or process400may be tightened (e.g., thresholds are made stricter) by comparing images having selected pose and/or distance criteria. For example, template space220, shown inFIG. 6, may include static templates216at multiple different angles or ranges of angles for the user in the images represented by the static templates. The angles or ranges of angles in template space220may be known. When images are captured during process300and/or process400, pose and/or distance information in a captured image may be used to assess an angle of the user in the captured image. The assessed angle may then be used to compare the captured image to static templates216having a similar angle or angle range. Comparing the captured image to the template image(s) having similar angles may allow the threshold for matching between the images to be increased. In some embodiments, captured images with a more frontal view of the user (e.g., the user's pose is substantially facing the camera) may be more preferentially used as the signals in such images tends to be higher than the signals in images with the user in more of a side view pose.

In certain embodiments, one or more processes are used after image enrollment process200, shown inFIG. 4, to assess and remove facial signatures (e.g., feature vectors) that may have resulted from a switch in identities (e.g., subjects) during the image enrollment process. For example, assessment of the clustering of feature vectors in the feature space may be used to remove one or more feature vectors that result from a switch in identities.FIG. 9depicts a representation of an embodiment of a clustering assessment process. In certain embodiments, process450is used to assess clustering of feature vectors in the feature space (e.g., feature space212, shown inFIG. 5) to remove outlying feature vectors after image enrollment process200is completed/finished, shown inFIG. 4.

As shown inFIG. 9, process450may begin with assessing clusters of feature vectors in the feature space in452.FIG. 10depicts a representation of an embodiment of feature space212with a plurality of feature vectors210in the feature space after image enrollment process200is completed. In certain embodiments, clusters of feature vectors210(represented by circles460inFIG. 10) are determined by assessment of the distribution of feature vectors in feature space212. For example, statistical analysis of feature vectors210in feature space212may be used to assess clustering of the feature vectors and determine clusters460. Statistical analysis may include, for example, classification algorithms operated on the feature vectors in the feature space.

In some embodiments, clusters460are defined based on characteristics of feature vectors210in feature space212. For example, feature vectors may define facial features of the user's face in the captured images in a feature space. The feature vectors defining the facial features may then be assessed (e.g., decoded and/or classified) to determine pose and/or distance information for the captured images. Clusters460may then be defined based on unique characteristics from the pose and/or distance information. Using the pose and/or distance information may provide better definition of grouping for clusters460in feature space212.

Clusters460may be, for example, groupings of feature vectors210that represent substantially similar facial signatures in feature space212. In certain embodiments, feature vectors210inside clusters460are assessed as being feature vectors that represent the same subject (e.g., the clusters represent facial signatures for the user being authorized by the image enrollment process). Feature vectors210outside clusters460(e.g., outlying feature vectors210′ or outliers) may be assessed as being feature vectors that do not represent the user being authorized by the image enrollment process.

After assessment of clusters460in feature space212, process450, shown inFIG. 9, may continue with the removal of the outliers (e.g., feature vectors210′) from the feature space in454.FIG. 11depicts a representation of an embodiment of feature space212′ after the removal of outlying feature vectors. As shown inFIG. 11, feature space212′ (e.g., the updated feature space) only includes feature vectors210found in clusters460. After the removal of the outliers, process450, as shown inFIG. 9, may continue with storing the updated feature space (feature space212′) in the device (e.g., storing the updated feature space in a memory of the device) in456. Feature space212′, as shown inFIG. 11, may then be used as the feature space with feature vectors for the authorized user in additional facial recognition processes on the device (e.g., a facial recognition authorization process).

Typically, the difference between facial signatures for a single subject (e.g., the authorized user) under different poses/motions is less than the difference between facial signatures for different subjects in the same pose/motion. Thus, removing the outlying feature vectors210′ (the outliers) may be more likely to remove feature vectors for a different subject than feature vectors for a different pose. Removing the outlying feature vectors210′ (whether they result from a different subject or some other factor) may improve the accuracy of a facial recognition authorization process using feature space212′. For example, removing outlying feature vectors210′ may remove the likelihood of false positives in the facial recognition authorization process using feature space212′.

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, process200, process300, process400, and process450, shown inFIGS. 4, 7, 8, and 9, 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. 12depicts a block diagram of one embodiment of exemplary computer system510. Exemplary computer system510may be used to implement one or more embodiments described herein. In some embodiments, computer system510is operable by a user to implement one or more embodiments described herein such as process200, process300, process400, and process450shown inFIGS. 4, 7, 8, and 9. In the embodiment ofFIG. 12, computer system510includes processor512, memory514, and various peripheral devices516. Processor512is coupled to memory514and peripheral devices516. Processor512is configured to execute instructions, including the instructions for process200, process300, process400, and/or process450, which may be in software. In various embodiments, processor512may 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 system510may include more than one processor. Moreover, processor512may include one or more processors or one or more processor cores.

Processor512may be coupled to memory514and peripheral devices516in any desired fashion. For example, in some embodiments, processor512may be coupled to memory514and/or peripheral devices516via various interconnect. Alternatively or in addition, one or more bridge chips may be used to coupled processor512, memory514, and peripheral devices516.

Memory514may comprise any type of memory system. For example, memory514may comprise DRAM, and more particularly double data rate (DDR) SDRAM, RDRAM, etc. A memory controller may be included to interface to memory514, and/or processor512may include a memory controller. Memory514may store the instructions to be executed by processor512during use, data to be operated upon by the processor during use, etc.

Peripheral devices516may represent any sort of hardware devices that may be included in computer system510or coupled thereto (e.g., storage devices, optionally including computer accessible storage medium600, shown inFIG. 13, other input/output (I/O) devices such as video hardware, audio hardware, user interface devices, networking hardware, etc.).

Turning now toFIG. 13, a block diagram of one embodiment of computer accessible storage medium600including one or more data structures representative of device100(depicted inFIG. 1) included in an integrated circuit design and one or more code sequences representative of process200, process300, process400, and/or process450(shown inFIGS. 4, 7, 8, and 9). 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 medium600may 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.

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.