Patent Publication Number: US-2021182585-A1

Title: Methods and systems for displaying a visual aid

Description:
BACKGROUND OF THE INVENTION 
     This invention relates generally to capturing user image data, and more particularly, to methods and systems for displaying a visual aid while capturing user image data. 
     Users conduct transactions with many different service providers in person and remotely over the Internet. Network-based transactions conducted over the Internet may involve purchasing items from a merchant web site or accessing confidential information from a web site. Service providers that own and operate such websites typically require successfully identifying users before allowing a desired transaction to be conducted. 
     Users are increasingly using smart devices to conduct such network-based transactions and to conduct network-based biometric authentication transactions. Some network-based biometric authentication transactions have more complex biometric data capture requirements which have been known to be more difficult for users to comply with. For example, some users have been known to position the smart device near their waist when capturing a facial image. Many users still look downwards even if the device is held somewhere above waist level. Such users typically do not appreciate that differently positioning the smart device should result in capturing better image data. Consequently, capturing image data of a biometric modality of such users that can be used for generating trustworthy authentication transaction results has been known to be difficult, annoying, and time consuming for users and authentication service providers. Additionally, capturing such image data has been known to increase costs for authentication service providers. 
     BRIEF DESCRIPTION OF THE INVENTION 
     In one aspect, a method for displaying a visual aid that includes calculating a distortion score based on an initial position of a computing device, and comparing, by the computing device, the distortion score against a threshold distortion value. When the distortion score is less than or equal to the threshold distortion value, a visual aid having a first size is displayed and when the distortion score exceeds the threshold distortion value the visual aid is displayed at a second size. 
     In another aspect, a computing device for displaying a visual aid is provided that includes a processor and a memory configured to store data. The computing device is associated with a network and the memory is in communication with the processor and has instructions stored thereon which, when read and executed by the processor, cause the computing device to calculate a distortion score based on an initial position of the computing device and compare the distortion score against a threshold distortion value. When the distortion score is less than or equal to the threshold distortion value a visual aid having a first size is displayed and when the distortion score exceeds the threshold distortion value the visual aid is displayed at a second size. 
     In yet another aspect, a method for displaying a visual aid is provided that includes establishing limits for a change in image data distortion. The method also includes calculating a distance ratio for each limit, calculating a width of a visual aid based on the maximum distance ratio, and displaying the visual aid. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram of an example computing device used for displaying a visual aid; 
         FIG. 2  is a side view of a person operating the computing device in which the computing device is in an example initial position; 
         FIG. 3  is an enlarged front view of the computing device displaying a facial image of the user when the computing device is in the initial position; 
         FIG. 4  is an enlarged front view of the computing device as shown in  FIG. 3 , further displaying a first example visual aid; 
         FIG. 5  is a side view of the user operating the computing device in which the computing device is in a first example terminal position; 
         FIG. 6  is an enlarged front view of the computing device in the first terminal position displaying the facial image approximately aligned with the first visual aid; 
         FIG. 7  is an enlarged front view of the computing device as shown in  FIG. 6 ; however, the facial image and visual aid are larger; 
         FIG. 8  is an enlarged front view of the computing device displaying the first visual aid as shown in  FIG. 7 ; 
         FIG. 9  is a side view of the user operating the computing device in which the computing device is in a second example initial position; 
         FIG. 10  is an enlarged front view of the computing device displaying the facial image of the user when the computing device is in the second example initial position; 
         FIG. 11  is an enlarged front view of the computing device displaying the facial image and a second example visual aid; 
         FIG. 12  is a side view of the user operating the computing device in a second example terminal position; 
         FIG. 13  is an enlarged front view of the computing device in the second example terminal position displaying the facial image approximately aligned with the second visual aid; 
         FIG. 14  is an example curve illustrating the rate of change in the distortion of biometric characteristics included in captured facial image data; 
         FIG. 15  is the example curve as shown in  FIG. 14  further including an example change in distortion; 
         FIG. 16  is the example curve as shown in  FIG. 15 ; however, the initial position of the computing device is different; 
         FIG. 17  is the example curve as shown in  FIG. 15 ; however, the terminal position is not coincident with the position of a threshold distortion value; 
         FIG. 18  is the example curve as shown in  FIG. 17 ; however, the change in distortion occurs between different limits; 
         FIG. 19  is the example curve as shown in  FIG. 18 ; however, the change in distortion occurs between different limits; 
         FIG. 20  is the example curve as shown in  FIG. 19 ; however, the change in distortion occurs between different limits; 
         FIG. 21  is a flowchart illustrating an example method of displaying a visual aid; and. 
         FIG. 22  is a flowchart illustrating another example method of displaying a visual aid. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a diagram of an example computing device  10  used for displaying a visual aid. The computing device  10  includes components such as, but not limited to, one or more processors  12 , a memory  14 , a gyroscope  16 , one or more accelerometers  18 , a bus  20 , a camera  22 , a user interface  24 , a display  26 , a sensing device  28 , and a communications interface  30 . General communication between the components in the computing device  10  is provided via the bus  20 . 
     The computing device  10  may be any device capable of at least capturing image data, processing the captured image data, and performing at least the functions described herein. One example of the computing device  10  is a smart phone. Other examples include, but are not limited to, a cellular phone, a tablet computer, a phablet computer, a laptop computer, a personal computer (PC), and any type of device having wired or wireless networking capabilities such as a personal digital assistant (PDA). 
     The processor  12  executes instructions, or computer programs, stored in the memory  14 . As used herein, the term processor is not limited to just those integrated circuits referred to in the art as a processor, but broadly refers to a computer, a microcontroller, a microcomputer, a programmable logic controller, an application specific integrated circuit, and any other programmable circuit capable of executing at least a portion of the functions and/or methods described herein. The above examples are not intended to limit in any way the definition and/or meaning of the term “processor.” 
     As used herein, the term “computer program” is intended to encompass an executable program that exists permanently or temporarily on any non-transitory computer-readable recordable medium that causes the computing device  10  to perform at least a portion of the functions and/or methods described herein. Application programs  32 , also known as applications, are computer programs stored in the memory  14 . Application programs  32  include, but are not limited to, an operating system, an Internet browser application, enrolment applications, authentication applications, user liveness detection applications, face tracking applications, applications that use pre-trained models based on machine learning algorithms, feature vector generator applications, and any special computer program that manages the relationship between application software and any suitable variety of hardware that helps to make-up a computer system or computing environment. 
     Authentication applications enable the computing device  10  to conduct user verification and identification (1:N) transactions with any type of authentication data, where “N” is a number of candidates. Machine learning algorithm applications include at least classifiers and regressors. Examples of machine learning algorithms include, but are not limited to, support vector machine learning algorithms, decision tree classifiers, linear discriminant analysis learning algorithms, and artificial neural network learning algorithms. Decision tree classifiers include, but are not limited to, random forest algorithms. 
     The memory  14  may be any non-transitory computer-readable recording medium used to store data including, but not limited to, computer programs and user data records. Non-transitory computer-readable recording media may be any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information or data. Moreover, the non-transitory computer-readable recording media may be implemented using any appropriate combination of alterable, volatile or non-volatile memory or non-alterable, or fixed, memory. The alterable memory, whether volatile or non-volatile, can be implemented using any one or more of static or dynamic RAM (Random Access Memory), a floppy disc and disc drive, a writeable or re-writeable optical disc and disc drive, a hard drive, flash memory or the like. Similarly, the non-alterable or fixed memory can be implemented using any one or more of ROM (Read-Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), an optical ROM disc, such as a CD-ROM or DVD-ROM disc, and disc drive or the like. Furthermore, the non-transitory computer-readable recording media may be implemented as smart cards, SIMs, any type of physical and/or virtual storage, or any other digital source such as a network or the Internet from which a computing device can read computer programs, applications or executable instructions. 
     The data records are typically for users associated with the computing device  10 . The data record for each user may include biometric modality data, biometric templates and personal data of the user. Biometric modalities include, but are not limited to, voice, face, finger, iris, palm, and any combination of these or other modalities. Biometric modality data is the data of a biometric modality of a person captured by the computing device  10 . As used herein, capture means to record temporarily or permanently, biometric modality data of a person. Biometric modality data may be in any form including, but not limited to, image data and audio data. Image data may be a digital image, a sequence of digital images, or a video. Each digital image is included in a frame. The biometric modality data in the data record may be processed to generate at least one biometric modality template. 
     The process of verifying the identity of a user is known as a verification transaction. Typically, during a verification transaction a biometric template is generated from biometric modality data of a user captured during the transaction. The generated biometric template is compared against the corresponding record biometric template of the user and a matching score is calculated for the comparison. If the matching score meets or exceeds a threshold score, the identity of the user is verified as true. Alternatively, the captured user biometric modality data may be compared against the corresponding record biometric modality data to verify the identity of the user. 
     An authentication data requirement is the biometric modality data desired to be captured during a verification or identification transaction. For the example methods described herein, the authentication data requirement is for the face of the user. However, the authentication data requirement may alternatively be for any biometric modality or any combination of biometric modalities. 
     Biometric modality data may be captured in any manner. For example, for voice biometric data the computing device  10  may record a user speaking. For face biometric data, the camera  22  may record image data of the face of a user by taking one or more photographs or digital images of the user, or by taking a video of the user. The camera  22  may record a sequence of digital images at irregular or regular intervals. A video is an example of a sequence of digital images being captured at a regular interval. Captured biometric modality data may be temporarily or permanently stored in the computing device  10  or in any device capable of communicating with the computing device  10 . Alternatively, the biometric modality data may not be stored. 
     When a sequence of digital images is captured, the computing device  10  may extract images from the sequence and assign a time stamp to each extracted image. The rate at which the computing device  10  extracts images is the image extraction rate. An application, for example a face tracker application, may process the extracted digital images. The image processing rate is the number of images that can be processed within a unit of time. Some images may take more or less time to process so the image processing rate may be regular or irregular, and may be the same or different for each authentication transaction. The number of images processed for each authentication transaction may vary with the image processing rate. The image extraction rate may be greater than the image processing rate so some of the extracted images may not be processed. The data for a processed image may be stored in the memory  14  with other data generated by the computing device  10  for that processed image, or may be stored in any device capable of communicating with the computing device  10 . 
     The gyroscope  16  and the one or more accelerometers  18  generate data regarding rotation and translation of the computing device  10  that may be communicated to the processor  12  and the memory  14  via the bus  20 . The computing device  10  may alternatively not include the gyroscope  16  or the accelerometer  18 , or may not include either. 
     The camera  22  captures image data. The camera  22  can be one or more imaging devices configured to record image data of at least a portion of the body of a user including any biometric modality of the user while utilizing the computing device  10 . Moreover, the camera  22  is capable of recording image data under any lighting conditions including infrared light. The camera  22  may be integrated into the computing device  10  as one or more front-facing cameras and/or one or more rear facing cameras that each incorporates a sensor, for example and without limitation, a CCD or CMOS sensor. Alternatively, the camera  22  can be external to the computing device  10 . 
     The user interface  24  and the display  26  allow interaction between a user and the computing device  10 . The display  26  may include a visual display or monitor that displays information to a user. For example, the display  26  may be a Liquid Crystal Display (LCD), active matrix display, plasma display, or cathode ray tube (CRT). The user interface  24  may include a keypad, a keyboard, a mouse, an illuminator, a signal emitter, a microphone, and/or speakers. 
     Moreover, the user interface  24  and the display  26  may be integrated into a touch screen display. Accordingly, the display may also be used to show a graphical user interface, which can display various data and provide “forms” that include fields that allow for the entry of information by the user. Touching the screen at locations corresponding to the display of a graphical user interface allows the person to interact with the computing device  10  to enter data, change settings, control functions, etc. Consequently, when the touch screen is touched, the user interface  24  communicates this change to the processor  12 , and settings can be changed or user entered information can be captured and stored in the memory  14 . The display  26  may function as an illumination source to apply illumination to a biometric modality while image data for the biometric modality is captured. 
     For user interfaces  24  that include an illuminator, the illuminator may project visible light, infrared light or near infrared light on a biometric modality, and the camera  22  may detect reflections of the projected light off the biometric modality. The reflections may be off of any number of points on the biometric modality. The detected reflections may be communicated as reflection data to the processor  12  and the memory  14 . The processor  12  may use the reflection data to create at least a three-dimensional model of the biometric modality and a sequence of two-dimensional digital images. For example, the reflections from at least thirty thousand discrete points on the biometric modality may be detected and used to create a three-dimensional model of the biometric modality. Alternatively, or additionally, the camera  22  may include the illuminator. 
     The sensing device  28  may include Radio Frequency Identification (RFID) components or systems for receiving information from other devices. The sensing device  28  may alternatively, or additionally, include components with Bluetooth, Near Field Communication (NFC), infrared, or other similar capabilities. The computing device  10  may alternatively not include the sensing device  28 . 
     The communications interface  30  provides the computing device  10  with two-way data communications. Moreover, the communications interface  30  enables the computing device  10  to conduct wireless communications such as cellular telephone calls and to wirelessly access the Internet over a network  34 . By way of example, the communications interface  30  may be a digital subscriber line (DSL) card or modem, an integrated services digital network (ISDN) card, a cable modem, or a telephone modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communications interface  30  may be a local area network (LAN) card (e.g., for Ethernet™ or an Asynchronous Transfer Model (ATM) network) to provide a data communication connection to a compatible LAN. As yet another example, the communications interface  30  may be a wire or a cable connecting the computing device  10  with a LAN, or with accessories such as, but not limited to, other computing devices. Further, the communications interface  30  may include peripheral interface devices, such as a Universal Serial Bus (USB) interface, a PCMCIA (Personal Computer Memory Card International Association) interface, and the like. Thus, it should be understood the communications interface  30  may enable the computing device  10  to conduct any type of wireless or wired communications such as, but not limited to, accessing the Internet. Although the computing device  10  includes a single communications interface  30 , the computing device  10  may alternatively include multiple communications interfaces  30 . 
     The communications interface  30  also allows the exchange of information across the network  34 . The exchange of information may involve the transmission of radio frequency (RF) signals through an antenna (not shown). Moreover, the exchange of information may be between the computing device  10  and any other computer systems  36  and any other computing devices  38  capable of communicating over the network  34 . The computer systems  36  and the computing devices  38  typically include components similar to the components included in the computing device  10 . The network  34  may be a 5G communications network. Alternatively, the network  34  may be any wireless network including, but not limited to, 4G, 3G, Wi-Fi, Global System for Mobile (GSM), Enhanced Data for GSM Evolution (EDGE), and any combination of a LAN, a wide area network (WAN) and the Internet. The network  34  may also be any type of wired network or a combination of wired and wireless networks. 
     Examples of other computer systems  36  include computer systems of service providers such as, but not limited to, financial institutions, medical facilities, national security agencies, merchants, and authenticators. Examples of other computing devices  38  include, but are not limited to, smart phones, tablet computers, phablet computers, laptop computers, personal computers and cellular phones. The other computing devices  38  may be associated with any individual or with any type of entity including, but not limited to, commercial and non-commercial entities. The computing devices  10 ,  38  may alternatively be referred to as computer systems or information systems, while the computer systems  36  may alternatively be referred to as computing devices or information systems. 
       FIG. 2  is a side view of a person  40  operating the computing device  10  in which the computing device  10  is in an example initial position at a distance D from the face of the person  40 . The initial position is likely to be the position in which a person naturally holds the computing device  10  to begin capturing facial image data of his or her self. Because people have different natural tendencies, the initial position of the computing device  10  is typically different for different people. The person  40  from whom facial image data is captured is referred to herein as a user. The user  40  typically operates the computing device  10  while capturing image data of his or her self. However, a person different than the user  40  may operate the computing device  10  while capturing image data. 
       FIG. 3  is an enlarged front view of the computing device  10  displaying a facial image  42  of the user  40  when the computing device  10  is in the example initial position. The size of the displayed facial image  42  increases as the distance D decreases and decreases as the distance D increases. 
     While in the initial position, the computing device  10  captures facial image data of the user and temporarily stores the captured image data in a buffer. Typically, the captured image data is a digital image. The captured facial image data is analyzed to calculate the center-to-center distance between the eyes which may be doubled to estimate the width of the head of the user  40 . The width of a person&#39;s head is known as the bizygomatic width. Alternatively, the head width may be estimated in any manner. Additionally, the captured facial image data is analyzed to determine whether or not the entire face of the user is in the image data. When the entire face of the user is in the captured image data, the buffered image data is discarded, a visual aid is displayed, and liveness detection is conducted. 
       FIG. 4  is an enlarged front view of the computing device  10  as shown in  FIG. 3 , further displaying an example visual aid  44 . The example visual aid  44  is an oval with ear-like indicia  46  located to correspond approximately to the ears of the user  40 . Alternatively, any other type indicia may be included in the visual aid  44  that facilitates approximately aligning the displayed facial image  42  and visual aid  44 . Other example shapes of the visual aid  44  include, but are not limited to, a circle, a square, a rectangle, and an outline of the biometric modality desired to be captured. The visual aid  44  may be any shape defined by lines and/or curves. Each shape may include the indicia  46 . The visual aid  44  is displayed after determining the entire face of the user is in the captured image data. The visual aid  44  is displayed to encourage users to move the computing device  10  such that the facial image  42  approximately aligns with the displayed visual aid  44 . Thus, the visual aid  44  functions as a guide that enables users to quickly capture facial image data usable for enhancing the accuracy of user liveness detection and generating trustworthy and accurate verification transaction results. 
     Most users intuitively understand that the displayed facial image  42  should approximately align with the displayed visual aid  44 . As a result, upon seeing the visual aid  44  most users move the computing device  10  and/or his or her self so that the displayed facial image  42  and visual aid  44  approximately align. However, some users  40  may not readily understand the displayed facial image  42  and visual aid  44  are supposed to approximately align. Consequently, a message may additionally, or alternatively, be displayed that instructs users to approximately align the displayed facial image  42  and visual aid  44 . Example messages may request the user to move closer or further away from the computing device  10 , or may instruct the user to keep his or her face within the visual aid  44 . Additionally, the message may be displayed at the same time as the visual aid  44  or later, and may be displayed for any period of time, for example, two seconds. Alternatively, the message may be displayed until the displayed facial image  42  and visual aid  44  approximately align. Additionally, the area of the display  26  outside the visual aid  44  may be made opaque or semi-transparent in order to enhance the area within which the displayed facial image  42  is to be arranged. 
       FIG. 5  is a side view of the user  40  operating the computing device  10  in which the computing device  10  is in a first example terminal position. The first terminal position is closer to the user  40  so the distance D is less than that shown in  FIG. 2 . After the visual aid  44  is displayed, typically users move the computing device  10 . When the computing device  10  is moved such that the facial image  42  approximately aligns with the displayed visual aid  44 , the computing device  10  is in the first terminal position. 
       FIG. 6  is an enlarged front view of the computing device  10  in the first example terminal position displaying the facial image  42  approximately aligned with the visual aid  44 . Generally, the displayed facial image  42  should be close to, but not outside, the visual aid  44  in the terminal position. However, a small percentage of the facial image  42  may be allowed to extend beyond the border. A small percentage may be between about zero and ten percent. 
     Users  40  may move the computing device  10  in any manner from any initial position to any terminal position. For example, the computing device  10  may be translated horizontally and/or vertically, rotated clockwise and/or counterclockwise, moved through a parabolic motion, and/or any combination thereof. Regardless of the manner of movement or path taken from an initial position to a terminal position, the displayed facial image  42  should be within the visual aid  44  during movement because the computing device  10  captures facial image data of the user  40  while the computing device  10  is moving. 
     The captured facial image data is temporarily stored in a buffer of the computing device  10  for liveness detection analysis. Alternatively, the captured image data may be transmitted from the computing device  10  to another computer system  36 , for example, an authentication computer system, and stored in a buffer therein. While capturing image data, the computing device  10  identifies biometric characteristics of the face included in the captured image data and calculates relationships between the characteristics. Such relationships may include the distance between characteristics. For example, the distance between the tip of the nose and a center point between the eyes, or the distance between the tip of the nose and the center of the chin. The relationships between the facial characteristics distort as the computing device  10  is moved closer to the face of the user  40 . Thus, when the computing device  10  is positioned closer to the face of the user  40  the captured facial image data is distorted more than when the computing device  10  is positioned further from the user  40 , say at arms-length. When the captured image data is transmitted to an authentication computer system, the authentication computer system may also identify the biometric characteristics, calculate relationships between the characteristics, and detect liveness based on, for example, distortions of the captured facial image data 
       FIG. 7  is an enlarged front view of the computing device  10  as shown in  FIG. 6 ; however, the facial image  42  and visual aid  44  are larger. The displayed facial image  42  is somewhat distorted as evidenced by the larger nose which occupies a proportionally larger part of the image  42  while the ear indicia  46  are narrower and thus occupy a smaller part of the image  42 . The facial image  42  also touches the top and bottom of the perimeter of the display  26 . 
     Face detector applications may not be able to properly detect a face in captured image data if the entire face is not included in the image data. Moreover, image data of the entire face is required for generating trustworthy and accurate liveness detection results. Thus, the displayed facial image  42  as shown in  FIG. 7  typically represents the maximum size of the facial image  42  for which image data can be captured and used to generate trustworthy and accurate liveness detection results. The position of the computing device  10  corresponding to the facial image  42  displayed in  FIG. 7  is referred to herein as the maximum size position. In view of the above, it should be understood that facial image data captured when the displayed facial image  42  extends beyond the perimeter of the display  26  typically is not used for liveness detection. However, facial image data captured when a small percentage of the displayed facial image  42  extends beyond the perimeter of the display  26  may be used for liveness detection. A small percentage may be between around one and two percent. 
       FIG. 8  is an enlarged front view of the computing device  10  displaying the visual aid  44  as shown in  FIG. 7 . However, the entire face of the user is not displayed and those portions of the face that are displayed are substantially distorted. The facial image  42  was captured when the computing device  10  was very close to the face of the user, perhaps within a few inches. Facial image data captured when the facial image is as shown in  FIG. 8  is not used for liveness detection because the entire face of the user is not displayed. 
       FIG. 9  is a side view of the user  40  operating the computing device  10  in which the computing device  10  is in a second example initial position which is closer to the face of the user  40  than the first initial position. 
       FIG. 10  is an enlarged front view of the computing device  10  displaying the facial image  42  when the computing device  10  is in the second example initial position. The second example initial position is in or around the maximum size position. 
       FIG. 11  is an enlarged front view of the computing device  10  displaying the facial image  42  and the example visual aid  44 . However, the visual aid  44  has a different size than that shown in  FIG. 4 . That is, the visual aid  44  is smaller than the visual aid  44  shown in  FIG. 4 . Thus, it should be understood that the visual aid  44  may be displayed in a first size and a second size where the first size is larger than the second size. It should be understood that the visual aid  44  may have a different shape in addition to being smaller. 
       FIG. 12  is a side view of the user  40  operating the computing device  10  in a second example terminal position after the computing device  10  has been moved away from the user. The computing device  10  is moved from the second initial position to the second terminal position in response to displaying the differently sized visual aid  44 . 
       FIG. 13  is an enlarged front view of the computing device  10  in the second example terminal position displaying the facial image  42  approximately aligned with the differently sized visual aid  44 . Facial image data captured while moving the computing device  10  from the second initial position to the second terminal position may also be temporarily stored in a buffer in the computing device  10  and used for detecting liveness. 
       FIG. 14  is an example curve  48  illustrating the rate of change in the distortion of biometric characteristics included in captured facial image data. The Y-axis corresponds to a plane parallel to the face of the user  40  and facilitates measuring the distortion, Y, of captured facial image data in one-tenth increments. The X-axis measures the relationship between the face of the user  40  and the computing device  10  in terms of a distance ratio R x . 
     The distance ratio R x  is a measurement that is inversely proportional to the distance D between the computing device  10  and the face of the user  40 . The distance ratio R x  may be calculated as the width of the head of the user  40  divided by the width of an image data frame at various distances D from the user  40 . Alternatively, the distance ratio R x  may be calculated in any manner that reflects the distance between the face of the user  40  and the computing device  10 . At the origin, the distance ratio R x  is 1.1 and decreases in the positive X direction in one-tenth increments. Thus, as the distance ratio R x  increases the distortion of captured facial image data increases and as the distance ratio R x  decreases the distortion of captured facial image data decreases. 
     Y MAX  occurs on the curve  48  at a point which represents the maximum distortion value for which captured image data may be used for detecting liveness, and corresponds to the distance ratio R x =1.0 which typically corresponds to the maximum size position as shown in  FIG. 7 . The example maximum distortion value is 0.28. However, it should be understood that the maximum distortion value Y MAX  varies with the computing device  10  used to capture the facial image data because the components that make up the camera  22  in each different computing device  10  are slightly different. As a result, images captured by different devices  10  have different levels of distortion and thus different maximum distortion values Y MAX . 
     The point (R xt , Y t ) on the curve  48  represents a terminal position of the computing device  10 , for example, the first terminal position. Y t  is the distortion value of facial image data captured in the terminal position. The distortion value Y t  should not equal Y MAX  because a user may inadvertently move the computing device  10  beyond Y MAX  during capture which will likely result in capturing faulty image data. As a result, a tolerance value c is used to enhance the likelihood that Y t  does not equal Y MAX  and that as a result quality image data only is captured. Quality image data may be used to enhance the accuracy and trustworthiness of liveness detection results and of authentication transaction results. 
     The tolerance value ε is subtracted from Y MAX  to define a threshold distortion value  50 . Captured facial image data having a distortion value less than or equal to the threshold distortion value  50  is considered quality image data, while captured facial image data with a distortion value greater than the threshold distortion value  50  is not. The tolerance value c may be any value that facilitates capturing quality image data, for example, any value between about 0.01 and 0.05. 
     The point (R xi , Y i ) on the curve  48  represents an initial position of the computing device  10 , for example, the first initial position. Y i  is the distortion value of facial image data captured in the initial position. The distortion values Y i  and Y t  are both less than the threshold distortion value  50 , so the image data captured while the computing device was in the initial and terminal positions is considered to be quality image data. Because the image data captured in the initial and terminal positions is considered quality image data, all facial image data captured between the initial and terminal positions is also considered to be quality image data. 
     Point  52  on the curve  48  represents the distortion value of facial image data captured when the computing device  10  is perhaps a few inches from the face of the user  40  as illustrated in  FIG. 8 . The distortion value at point  52  is greater than the threshold distortion value  50  so image data captured while the computing device  10  is a few inches from the face of the user  40  typically is not considered to be quality image data. 
     The distortion of captured image data may be calculated in any manner. For example, the distortion may be estimated based on the interalar and bizygomatic widths where the interalar width is the maximum width of the base of the nose. More specifically, a ratio R 0  between the interalar and bizygomatic widths of a user may be calculated that corresponds to zero distortion which occurs at Y=0.0. Zero distortion occurs at a theoretical distance D of infinity. However, as described herein zero distortion is approximated to occur at a distance D of about five feet. 
     The ratios R 0  and R x  may be used to estimate the distortion in image data captured at various distances D. The distortion at various distances D may be estimated as the difference between the ratios, R x −R 0 , divided by R 0 , that is (R x −R 0 )/R 0 . Alternatively, any other ratios may be used. For example, ratios may be calculated between the height of the head and the height of the nose, where the height of the head corresponds to the bizygomatic width. Additionally, it should be understood that any other type of calculation different than ratios may be used to estimate the distortion in image data. For the curve  48 , capture of facial image data may start at about two feet from the user  40  and end at the face of the user  40 . 
     For the example methods and systems described herein, trustworthy and accurate user liveness detection results may be calculated as a result of analyzing quality facial image data captured during a 0.1 change ΔY in distortion. Analyzing facial image data captured during a 0.1 change ΔY in distortion typically enables analyzing less image data which facilitates reducing the time required for conducting user liveness detection and thus enhances user convenience. 
       FIG. 15  is the example curve  48  as shown in  FIG. 14  further including a 0.1 change ΔY in distortion between the limits of Y=0.1 and Y=0.2. The change in distortion may be used to determine whether to display the large or small visual aid  44 . The distortion value Y i  and the 0.1 change ΔY in distortion may be summed, i.e., Y i +ΔY, to yield a distortion score Y s . The distortion value Y i  is 0.1 so the distortion score Y s  is 0.2. When the distortion score Y s  is less than or equal to the threshold distortion score  50 , the large visual aid  44  is displayed and all image data captured by the computing device  10  while moving from the initial position into the terminal position is considered quality image data. 
       FIG. 16  is the example curve  48  as shown in  FIG. 15 ; however, the initial position of the computing device  10  is different and results in a distortion score Ys that exceeds the threshold distortion value  50 . Because the distortion score Ys exceeds the threshold distortion value  50 , the 0.1 change ΔY in distortion value is subtracted from the initial distortion value Y i =0.22. As a result, the small visual aid  44  is displayed. Displaying the small visual aid  44  encourages moving the computing device  10  away from the face of the user  40 . 
       FIG. 17  is the example curve  48  as shown in  FIG. 15 ; however, the terminal position is not coincident with the position of the threshold distortion value  50 . Rather, the terminal position corresponds to the distortion score of Y s =0.2 which corresponds to the distance ratio R x =0.9. The initial position corresponds to the distortion value Y i =0.1 which corresponds to the distance ratio R x =0.7. Thus, the distance ratios are calculated as 0.9 and 0.7 which have a difference of 0.20. The 0.1 change ΔY in distortion also occurs between the limits of Y=0.1 and Y=0.2. The distortion score Y s  is 0.2 which is less than the threshold distortion value  50  so image data captured between the initial and terminal positions is considered quality image data. 
     Moving the computing device  10  between the distance ratios R x =0.7 and R x =0.9 enhances user convenience because the user is required to move the device  10  less while capturing image data. Moreover, less image data is typically captured which means it typically takes less time to process the data when detecting liveness which also enhances user convenience. 
     To facilitate capturing image data between the initial position at R x =0.7 and the terminal position at R x =0.9 only, a custom sized visual aid  44  may be displayed. When the distortion score Y s  is less than or equal to the threshold distortion value  50 , the size of the visual aid  44  is customized to have a width based on the greatest calculated distance ratio R x  which occurs in the terminal position. More specifically, because the distance ratio is calculated as the bizygomatic width divided by the width of an image data frame, the width of the custom visual aid at the terminal position can be calculated as the frame width multiplied by the greatest calculated distance ratio R x =0.90. 
     It should be understood that the 0.1 change ΔY in distortion may be positioned to occur anywhere along the Y-axis and that each position will have a different upper and lower limit. Because quality image data need be captured only during the 0.1 change ΔY in distortion, the upper and lower limits may be used to reduce or minimize the movement required to capture quality image data. More specifically, the 0.1 change ΔY in distortion may be positioned such that the limits reduce or minimize the difference between the distance ratios R x  in the initial and terminal positions. 
       FIG. 18  is the example curve  48  as shown in  FIG. 17 ; however, the 0.1 change ΔY in distortion occurs between the limits of Y=0.12 and Y=0.22. The corresponding distance ratios are R x =0.75 and R x =0.92. The difference between the distance ratios is 0.17. The 0.17 difference is 0.03 less than the 0.20 difference described herein with respect to  FIG. 17  which means the computing device  10  is moved through a shorter distance to capture quality image data. Moving the computing device through smaller differences in the distance ratio is preferred because less movement of the computing device  10  is required to capture quality image data, which enhances user convenience. 
       FIG. 19  is the example curve  48  as shown in  FIG. 18 ; however, the 0.1 change ΔY in distortion occurs between the limits of Y=0.22 and Y=0.32. The distortion score Y s  is 0.32 which is greater than the threshold distortion value  50 , so image data captured for the 0.1 change ΔY in distortion between Y=0.22 and Y=0.32 is not considered quality image data. As a result, the 0.1 change ΔY in distortion is subtracted from the distortion Y 1  and the width of the custom visual aid is calculated accordingly. 
       FIG. 20  is the example curve  48  as shown in  FIG. 19 ; however, the 0.1 change ΔY in distortion is subtracted from the distortion Y 1  such that the 0.1 change ΔY in distortion occurs between the limits of Y=0.12 and Y=0.22. The distortion values of Y=0.22 and Y=0.12 correspond to the distance ratios of R x =0.92 and R x =0.73. Thus, the calculated distance ratios are 0.92 and 0.73. When the 0.1 change ΔY in distortion is subtracted from the distortion value Y i , the smallest calculated distance ratio is used to calculate the width of the custom visual aid. That is, the distance score of 0.73 is multiplied by the image data frame width to yield the width of the custom visual aid. 
     Imposters have been known to use two-dimensional photographs of users during cyber-attacks. However, the facial characteristic distortions caused by moving a two-dimensional photograph towards and away from the computing device  10  are typically insignificant or are different than those that occur in facial image data captured of a live person. Thus, distortions in captured facial image data may be used as the basis for detecting user liveness. 
     After repeatedly capturing facial image data as a result of moving the computing device  10  between the same initial position and the same terminal position, users may become habituated to the movement so may try placing the computing device  10  in an initial position that is in or around the terminal position in an effort to reduce the time required for detecting liveness. However, doing so typically does not allow for detecting a 0.1 change ΔY in distortion because many times the distortion score Y s  exceeds the threshold distortion value  50 . Consequently, doing so usually results in displaying the small visual aid  44 . 
       FIG. 21  is a flowchart  52  illustrating an example method of displaying a visual aid. The method starts  54  by placing  56  the computing device  10  in an initial position at a distance D from the face of the user  40 , capturing  58  facial image data of the user  40 , and analyzing the captured facial image data. More specifically, the facial image data is analyzed to determine  60  whether or not the entire face of the user  40  is present in the captured facial image data. If the entire face is not present  60 , processing continues by capturing  58  facial image data of the user  40 . However, if the entire face is present  60 , processing continues by calculating  62  a distortion score Y s  and comparing the distortion score Y s  against the threshold distortion value  50 . If the distortion score Y s  is less than or equal to the threshold distortion value  50 , the computing device  10  continues by displaying  66  the visual aid  44  at a first size and capturing  68  facial image data of the user  40  while being moved from the initial to the terminal position. Next, processing ends  70 . However, if the distortion score Y s  exceeds the threshold distortion value  50 , the computing device  10  continues by displaying  72  the visual aid  44  at a second size and capturing  68  facial image data of the user while being moved from the initial to the terminal position. Next, processing ends  70 . 
       FIG. 22  is a flowchart  74  illustrating another example method of displaying a visual aid. This alternative example method is similar to that described herein with regard to  FIG. 21 ; however, after determining  64  whether or not the distortion score Y s  exceeds the threshold distortion value  50  the computing device displays a custom visual aid. More specifically, when the distortion score Y s  is calculated and is less than or equal to the threshold distortion value  50 , the computing device  10  continues by calculating  76  the distance ratios that correspond to the limits of the 0.1 change ΔY in distortion, calculating the width of the custom visual aid based on the greatest calculated distance ratio, and displaying  78  the custom visual aid with the calculated width while capturing  78  facial image data. Next, processing ends  80 . 
     However, when the distortion score Y s  exceeds the threshold distortion value  50 , the computing device  10  continues by subtracting the 0.1 change ΔY in distortion from the distortion value Y s , calculating the distance ratios corresponding to the limits of the 0.1 change ΔY in distortion, calculating  82  the width of the custom visual aid based on the smallest calculated distance ratio, and displaying  78  the custom visual aid with the calculated width while capturing  78  facial image data. Next, processing ends  80 . 
     The example methods described herein may be conducted entirely by the computing device  10 , or partly on the computing device  10  and partly on other computing devices  38  and computer systems  36  operable to communicate with the computing device  10  over the network  34 . Moreover, the example methods described herein may be conducted entirely on the other computer systems  36  and other computing devices  38 . Thus, the example methods may be conducted on any combination of computers, computer systems  36 , and computing devices  38 . Furthermore, data described herein as being stored in the memory  14  may alternatively be stored in any computer system  36  or computing device  38  operable to communicate with the computing device  10  over the network  34 . Additionally, the example methods described herein may be implemented with any number and organization of computer program components. Thus, the methods described herein are not limited to specific computer-executable instructions. Alternative example methods may include different computer-executable instructions or components having more or less functionality than described herein. 
     In example embodiments, the above-described methods and systems for displaying a visual aid enhance the accuracy and trustworthiness of user liveness detection results as well as verification transaction results. More specifically, in one example embodiment, after determining the entire face of a user is in captured image data, a computing device continues by calculating a distortion score and comparing the calculated distortion score against a threshold distortion value. If the distortion score is less than or equal to the threshold distortion value, the computing device continues by displaying a visual aid at a first size and capturing facial image data of the user while being moved from an initial position to a terminal position. However, if the distortion score exceeds the threshold distortion value, the computing device continues by displaying the visual aid at a second size and capturing facial image data of the user while being moved from the initial to the terminal position. 
     In another example embodiment, after determining whether or not the distortion score exceeds the threshold distortion value the computing device displays a custom visual aid. When the distortion score is calculated and is less than or equal to the threshold distortion value, the computing device continues by calculating the distance ratios that correspond to the limits of the 0.1 change ΔY in distortion, calculating the width of the custom visual aid based on the greatest calculated distance ratio, and displaying the custom visual aid with the calculated width while capturing facial image data. However, when the distortion score exceeds the threshold distortion value, the computing device continues by subtracting the 0.1 change ΔY in distortion from the distortion value, calculating the distance ratios corresponding to the limits of the 0.1 change ΔY in distortion, calculating the width of the custom visual aid based on the smallest calculated distance ratio, and displaying the custom visual aid with the calculated width while capturing facial image data. 
     As a result, in each example embodiment, image data is captured quickly and conveniently from users which may be used to facilitate enhancing detection of spoofing attempts, accuracy and trustworthiness of user liveness detection results and of verification transaction results, and reducing time wasted and costs incurred due to successful spoofing and faulty verification transaction results. Additionally, user convenience for capturing image data with computing devices is enhanced. 
     The example methods and systems for displaying a visual aid described above should not be considered to imply a fixed order for performing the method steps. Rather, the method steps may be performed in any order that is practicable, including simultaneous performance of at least some steps. Moreover, the method steps may be performed in real time or in near real time. It should be understood that, for any process described herein, there can be additional, fewer, or alternative steps performed in similar or alternative orders, or in parallel, within the scope of the various embodiments, unless otherwise stated. Furthermore, the invention is not limited to the embodiments of the methods described above in detail. Rather, other variations of the methods may be utilized within the spirit and scope of the claims.