Patent Publication Number: US-2021182535-A1

Title: Method for selecting images in video of faces in the wild

Description:
BACKGROUND 
     Technical Field 
     The present disclosure relates to systems and methods for processing facial images. 
     Description of the Related Art 
     Some processor-based systems are configured to process images in order to recognize objects, persons, places, animals, or other such features depicted. Previously, users of some image processing systems had to manually select which images to process because the processing power of such systems is limited. However, in situations where there is a large amount of image data, it becomes impracticable to process a substantial amount of data to recognize particular objects or persons. For instance, in public spaces, such as airports and city streets, the amount of human traffic is far too large for any reasonable number of users to select which images to process in an acceptable time period. Moreover, it may be difficult and time consuming for a user to select which depictions of persons or objects in the images are suitable for processing. This problem becomes even more challenging with video data that includes a series of images as the users are required to process the video data in real-time. Existing computer systems are insufficiently equipped or configured to solve this problem, and thus the ability of image processing systems is significantly limited in such situations. 
     BRIEF SUMMARY 
     A computer-implemented method may be summarized as including receiving a plurality of images; and for each of one or more face images in the plurality of images: generating assessment information that includes contrast information regarding a contrast quality of the face image; comparing the assessment information for the face image with a set of criteria that includes a contrast quality criterion; determining, as a result of comparing the assessment information with the set of criteria, whether the assessment information associated with the face image satisfies the set of criteria; and identifying, in response to determining that the assessment information satisfies the set of criteria, the face image as being qualified for facial recognition processing. 
     The assessment information may include sharpness information regarding a sharpness of the face image, the set of criteria may include a sharpness criterion, and determining that the assessment information satisfies the set of criteria may include determining that the sharpness information satisfies the sharpness criterion. Generating the sharpness information may include performing a downsampling operation on the face image and determining a sharpness differential between the face image and a result of the downsampling operation, and determining that the assessment information satisfies the set of criteria may include determining that the sharpness differential satisfies the sharpness criterion. Generating the contrast information may include identifying a plurality of features in the face image that correspond to predetermined facial features; determining a plurality of regions of the face image that respectively surround a feature of the plurality of features; assigning contrast values to each region of the plurality of regions based on a maximum pixel contrast of pixels in each region; and determining a sum of the contrast values of the plurality of regions. Determining that the assessment information satisfies the set of criteria may include determining that the sum of the contrast values satisfies the contrast criterion. The set of criteria may include an orientation criterion, and generating the assessment information may include generating orientation information that includes estimating an orientation of the face in the face image, and determining that the assessment information satisfies the set of criteria may include determining that the orientation information satisfies the orientation criterion. The assessment information may include size information identifying one or more size dimensions of the face image, the set of criteria may include a size criterion, and determining that the assessment information satisfies the set of criteria may include determining that the size information satisfies a size threshold in the set of criteria. 
     The face image may be a first face image, the assessment information may be first assessment information including first contrast information, and the method may further include obtaining a second face image; generating second assessment information of the second face image that includes second contrast information regarding a contrast quality of the second face image; determining that the second assessment information satisfies a second set of criteria that includes a second facial contrast quality criterion; and generating, as a result of determining that the second assessment information satisfies the second set of criteria, a biometric signature of the second face image. 
     The computer-implemented method may further include generating a first biometric signature of the first face image and comparing the first biometric signature of the first face image to the second biometric signature of the second face image. Each criterion of the second set of criteria may include different values than each corresponding criterion of the set of criteria. 
     The computer-implemented method may further include storing the biometric signature as a result of determining that the second assessment information represents an improvement over a stored biometric image. 
     The second face image may be obtained at a second time and the method may further include storing a biometric signature of the first face image as being associated with an identity in data storage at a first time previous to the second time; and storing the biometric signature of the second face image as a result of a determination that a time period between the first time and the second time exceeds a predetermined threshold. 
     The computer-implemented method may further include generating a biometric signature of the face image and a second biometric signature for a second face image consecutive to the face image in the plurality of images; and determining an image differential based on a comparison of the biometric signature and the second biometric signature, wherein analyzing the face in the face image is performed as a result of the image differential being less than a predetermined threshold for image consistency. 
     The method may further include detecting face images in at least a subset of images of the plurality of images, wherein generating the assessment information is in response to detecting the face image in the subset of images. 
     The method may further include analyzing the face in the face image to determine an identity of a person corresponding to the face. 
     A system, may be summarized as including one or more processors; and non-transitory storage medium storing a set of instructions that, as a result of execution by the one or more processors, cause the system to: receive a plurality of images; and for each of one or more face images in the plurality of images: generate assessment information that includes contrast information regarding a contrast quality of the face image; compare the assessment information with a set of criteria that includes a contrast quality criterion; and generate, as a result of determining that the assessment information satisfies the set of criteria, result information indicating that the assessment information associated with the face image satisfies the set of criteria. 
     The instructions, as a result of execution by the one or more processors, may further cause the system to transmit, to a second system, the result information for authorizing the second system to perform facial recognition processing using the face image. 
     The instructions, as a result of execution by the one or more processors, may further cause the system to generate a biometric signature of the face image; and compare, as a result of determining that the assessment information satisfies the set of criteria, the biometric signature with a plurality of biometric signatures stored in data storage. 
     The instructions, as a result of execution by the one or more processors, may further cause the system to determine, as a result of comparing the biometric signature with the plurality of biometric signatures, that the biometric signature does not correspond to a stored identity; and create a new identity corresponding to the biometric signature. 
     The instructions, as a result of execution by the one or more processors, may further cause the system to determine, as a result of comparing the biometric signature with the plurality of biometric signatures, that the biometric signature corresponds to a plurality of stored identities; and merge the plurality of identities into a single stored identity. The assessment information may include a size of the face image, pose information regarding an orientation of a face in the face image, and sharpness information regarding a sharpness of the face image, and the set of criteria may include a size criterion, an orientation criterion, and a sharpness criterion. 
     The instructions, as a result of execution by the one or more processors, may further cause the system to identify a plurality of features in the face image that correspond to predetermined facial features; determine a plurality of regions of the face image that respectively surround each feature of the plurality of features; compute maximum pixel difference values for the plurality of regions; and compute an average pixel difference of the maximum pixel difference values, wherein the contrast information corresponds to the average pixel difference. 
     A non-transitory computer-readable medium storing computer-executable instructions that, as a result of execution by a processor, may be summarized as causing the processor to obtain a plurality of images; and for each of one or more face images in the plurality of images: generate assessment information that includes contrast information regarding a contrast quality of the face image; compare the assessment information with a set of criteria that includes a contrast quality criterion; and generate, as a result of determining that the assessment information satisfies the set of criteria, result information indicating that the assessment information associated with the face image satisfies the set of criteria. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  shows a system for selecting, from a plurality of images, one or more subimages to be processed according to one or more embodiments; 
         FIG. 2  shows an environment in which one or more images are evaluated to determine whether to perform facial recognition processing; 
         FIG. 3  shows an environment in which image assessment information is evaluated; 
         FIG. 4  shows an environment in which the consistency of one or more images is verified; 
         FIG. 5  shows a diagram of a plurality of regions generated for a face image; 
         FIG. 6  shows a method for evaluating a plurality of images for facial recognition processing; 
         FIG. 7  shows a method for generating sharpness information regarding the face image; 
         FIG. 8  shows a method for determining a plurality of regions in the face image; 
         FIG. 9  shows a method for generating contrast quality information regarding the face image; 
         FIG. 10  shows a method for performing facial recognition processing; 
         FIG. 11  shows a method for updating contents of data storage involved in facial recognition processing; 
         FIG. 12  shows a method for verifying the consistency of images received; and 
         FIG. 13  shows a graphical representation representing a relationship of calculated values for contrast quality and normalized values for contrast quality. 
     
    
    
     DETAILED DESCRIPTION 
     The following description, along with the accompanying drawings, sets forth certain specific details in order to provide a thorough understanding of various disclosed embodiments. However, one skilled in the relevant art will recognize that the disclosed embodiments may be practiced in various combinations, without one or more of these specific details, or with other methods, components, devices, materials, etc. In other instances, well-known structures or components that are associated with the environment of the present disclosure, including but not limited to the communication systems and networks, have not been shown or described in order to avoid unnecessarily obscuring descriptions of the embodiments. Additionally, the various embodiments may be methods, systems, media, or devices. Accordingly, the various embodiments may be entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. 
     Throughout the specification, claims, and drawings, the following terms take the meaning explicitly associated herein, unless the context clearly dictates otherwise. The term “herein” refers to the specification, claims, and drawings associated with the current application. The phrases “in one embodiment,” “in another embodiment,” “in various embodiments,” “in some embodiments,” “in other embodiments,” and other variations thereof refer to one or more features, structures, functions, limitations, or characteristics of the present disclosure, and are not limited to the same or different embodiments unless the context clearly dictates otherwise. As used herein, the term “or” is an inclusive “or” operator, and is equivalent to the phrases “A or B, or both” or “A or B or C, or any combination thereof,” and lists with additional elements are similarly treated. The term “based on” is not exclusive and allows for being based on additional features, functions, aspects, or limitations not described, unless the context clearly dictates otherwise. In addition, throughout the specification, the meaning of “a,” “an,” and “the” include singular and plural references. 
     References to the term “set” (e.g., “a set of items”), as used herein, unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members or instances. 
     References to the term “subset” (e.g., “a subset of the set of items”), as used herein, unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members or instances of a set or plurality of members or instances. Moreover, the term “subset,” as used herein, refers to a collection of one or more members or instances that are collectively smaller in number than the set or plurality of which the subset is comprised. For instance, a subset of a set of ten items will include less than ten items and at least one item. 
     References to the term “module,” as used herein, is to be construed as a collection of hardware configured to perform a set of particular computing functions within a system. The hardware of a module may include one or more processors that are specifically hardwired to perform one or more of the set of particular computing functions. A module may include a general purpose processor and memory storing a set of instructions that, as a result of execution by the general purpose processor, causes the module to perform one or more of the set of particular computing functions. 
     References to the term “subimage,” as used herein, refers to a portion of a image. A subimage, for example, may be a collection of pixels taken from an image that comprise fewer pixels than the number of pixels of the entire image. 
       FIG. 1  shows a system  100  for selecting, from a plurality of images, one or more subimages to be processed according to one or more embodiments herein. The system  100  includes one or more processors  102  that cause the system  100  to perform one or more operations described herein. The system  100  receives a plurality of images  105  from a camera  106  and analyzes the plurality of images  105  to determine which, if any, face images  114  to select for use in connection with facial recognition operations. A face image  114  may be an entire image  104  that depicts a single face or may be a portion of an image  104  that depicts a single face and that is smaller than the entire image  104 . In some instances, a face image  114  refers to an image  104  or subimage thereof identified by the system  100  as containing an image depicting a single human face. 
     The system  100  evaluates the plurality of images  105  and determines which, if any, of the face images contained in the plurality of images  105  satisfy face image criteria  108 . Face images determined as satisfying the face image criteria  108  are subjected to further processing, which may include processes related to identification of a person shown in the face image. Because processes related to identification of a person (e.g., facial recognition) are resource intensive, determining face images that satisfy the face image criteria  108  helps to ensure that those face images having certain qualities correlated to a higher likelihood of identifying persons are processed for facial recognition. As a result, face images having less desirable qualities for face recognition are discarded, or at least not used for facial recognition, and the efficiency of resources directed to facial recognition processes may be improved. 
     A face detection process  110  is performed on the plurality of images  105  to identify a subset of images  112  (e.g., image  104 ) of the plurality of images  105  that include one or more face images. As part of or in connection with the face detection process  110 , the system  100  may identify face images  114  from the subset of images  112  and perform a face image assessment  116  on each of the face images  114 . In particular, the system  100  assesses each of the face images  114  based on a set of factors and generates image assessment information  118  for each of the face images  114  based on a result of the assessment. The face image  114  may be evaluated based on image characteristics as well as content of the face in the face image  114 . The set of factors may include one or more of a size of the face image  114 , a pose of a face in the face image  114 , sharpness of the face image  114 , and contrast quality of the face image  114 . 
     The image assessment information  118  generated is evaluated based on face image criteria  108  to determine whether to advance the face image  114  for facial recognition processing  122 . The face image criteria  108  includes a set of criteria corresponding to the aforementioned set of factors—namely, one or more of the size of the face image  114 , the pose of the face in the face image  114 , the sharpness of the face image  114 , and the contrast quality of the face image  114 . The set of criteria may specify a different threshold or condition corresponding to each of the set of factors. The system  100  compares  120  the information for each factor in the image assessment information  118  to the corresponding threshold or condition in the face image criteria  108 . As a result of determining that the image assessment information  118  satisfies each of the set of criteria in the face image criteria  108 , the system  100  advances the face image  114  for facial recognition processing  122 . Advancing the face image  114  for facial recognition processing  122  may include storing the face image  114  in data storage for further processing, transmitting the face image  114  to another part of the system  100  or another system for facial recognition processing  122 , or otherwise approving the face image  114  for facial recognition processing  122 . On the other hand, as a result of determining that the image assessment information  118  fails to satisfy each of the set of criteria, the system  100  will not advance the face image  114  for facial recognition processing  122  and may instead eliminate  124  the face image  114  from consideration. Facial recognition processing  122  is described in further detail below. 
     The system  100  may process each face image  114  identified in the subset of images  112  to determine whether to perform facial recognition processing  122  thereon. For each face image  114  approved for facial recognition processing  122 , the system  100  may store in persistent storage the data corresponding to the face image  114  and may optionally store the image assessment information  118  in association with the face image  114  data. Subsequent to evaluating all the face images  114  identified in an image  104  of the subset of images  112 , the system  100  may discard the image  104  and obtain another image  104  of the subset of images  112  to evaluate. The plurality of images  105  correspond to a series of images taken sequentially over time. The plurality of images  105 , for instance, may be a stream of video data, a video data object, or a collection of image data objects sequentially captured by the camera  106 . 
     The system  100  may continue to obtain images  104  (e.g., from the camera  106 , from another system) until a suitable face image  114  is identified for facial recognition processing  122  or until no more images  104  remain for processing. The system  100  may replace stored face image  114  with a face image  114  determined as having superior or better characteristics (e.g., sharpness, pose) than the stored face image  114 . In some instances, the biometric signature of a face image  114  determined as having superior characteristics may be stored in place of or in addition to the stored biometric signature of the face image  114  having less desirable characteristics. As a result, the system  100  is configured to obtain better images and biometric signatures for comparison, thereby improving the accuracy of the system  100  over time. Further description of the process of obtaining better samples for comparison is described below with respect to  FIG. 11 . 
     The camera  106 , from which the plurality of images  105  is obtained, is electrically communicatively coupled to the system  100  and provides the plurality of images  105  as an input to the system  100 . The camera  106  may be directly coupled to the system  100  via a wired connection or may be remotely coupled to provide the plurality of images  105  to the system  100 , for example, over a wired or wireless communication network. In some embodiments, a plurality of cameras  106  coupled to the system  100  may each provide a plurality of images  105  to the system  100 . Although the plurality of images  105  are shown as being received from the camera  106 , the plurality of images  105  may be received from a source other than the camera  106 . For instance, the plurality of images  105  may be received over a network (e.g., local area network, wide area network) and/or via an intermediate device, such as a network router or a server. In some instances, the plurality of images  105  may be stored in memory at a first time and provided to the system  100  at a second time later than the first time. 
     In some embodiments, the one or more processors  102  are electrically communicatively coupled to memory  126  that stores a set of instructions  128  that, as a result of execution by the one or more processors  102 , cause the system  100  to perform one or more operations described herein. In some embodiments, the one or more processors  102  may be a device having hardware specifically configured to perform at least some of the operations described herein. For instance, the one or more processors  102  may include application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), system-on-a-chip (SoC), or other specialized or customizable computing hardware hardwired to perform at least some of the operations described herein. 
       FIG. 2  shows an environment  200  in which one or more images  104  are evaluated by the system  100  to determine whether to perform facial recognition processing according to one or more embodiments described herein. The environment  200  includes a face detection module  202  that detects images of faces, also referred to as face images, in each of the plurality of images  105 . The face detection module  202  receives the plurality of images  105  provided by the camera  106 , processes each image  104  of the plurality of images  105 , and identifies images  104  containing one or more face images  114  of persons. The face detection module  202  implements one or more algorithms for detecting facial features in the images  104 . The face detection module  202  may, for instance, detect images of all or part of faces, such as images taken from the side that do not include both eyes of the subject. The face detection module  202  may detect the presence of a face in an image based on detecting a match between a group of pixels therein and attributes indicated as corresponding to human faces. The face detection module  202  may identify regions of pixels in each image  104  corresponding to a face. 
     The face detection module  202  may generate and provide output to an image assessment module  204  indicating that a face was detected in an image  104  received. The face detection module  202  may output the subset of images  112  containing at least one face image  114  to the image assessment module  204 . In some embodiments, the face detection module  202  may indicate, to the image assessment module  204 , which images  104  were detected as containing a face to the image assessment module  204  and information indicating one or more regions of pixels in the image  104  detected as containing the face. The image assessment module  204  may then extract each of the face images  114  from the regions of pixels identified for assessment. In some embodiments, the face detection module  202  may extract the groups of pixels detected as the face image  114 , and send the extracted face images  114  to the image assessment module  204 . 
     The image assessment module  204  receives or otherwise obtains the face images  114  indicated by the face detection module  202  and assesses the face images  114 . For each face image  114 , the image assessment module  204  generates image assessment information  118  regarding one or more of the set of factors discussed herein—the size of the face image  114 , the pose of the face in the face image  114 , the sharpness of the face image  114 , and the contrast quality of the face image  114 . The image assessment module  204  evaluates the image assessment information  118  using the face image criteria  108  to determine whether the face image  114  has attributes that justify subjecting the face image  114  to facial recognition processing  122 . 
     If the image assessment module  204  determines that the image assessment information  118  for a particular face image  114  satisfies the face image criteria  118 , the image assessment module  204  may generate an assessment output  206  that approves the face image  114  for facial recognition processing  122 . The output  206  may include the face image  114  or may include information indicating a location (e.g., in memory, network location, database location) from which the face image  114  may be obtained. The output  206  may include result information  208  indicating various information related to the assessment performed. For instance, the result information  208  may include some or all of the image assessment information  118 . The result information  208  may indicate information indicating a margin of satisfaction of the image assessment information  118  for each factor with respect to the face image criteria  108 —for instance, numerical values indicating how much the image assessment information  118  exceeded each factor of the face image criteria  108 . In some embodiments, the result information  208  may indicate one or more operations to be performed in the facial recognition processing  122  as a result of the assessment by the image assessment module  204 . If the image assessment module  204  determines that the image assessment information  118  does not satisfy the face image criteria  108 , the image assessment module  204  may discard the face image  114  and obtain another face image  114  for assessment. The image assessment module  204  may not generate or provide the output  206  for a face image  114  as a result of determining that the corresponding image assessment information  118  fails to satisfy the face image criteria  108 . 
     As a result of receiving the output  206  from the image assessment module  204 , an image processing module  210  may include performing facial recognition processing on the face image  114  in accordance with the output  206 . Performing facial recognition processing by the image processing module  210  may include generating a biometric signature  212  of the face image  114 , which can be used to determine an identity of the person depicted in the face image  114 . In some embodiments, the biometric signature  212  is a face template having features corresponding to features of a subject&#39;s face. In some embodiments, the image processing module  210  may be part of a second system that is separate from the system  100 . The second system, for example, may be separated from and connected to the system  100  by a network  218 , such as a local area network or a wide area network. 
     The image processing module  210  may perform various operations involving the biometric signature  212  depending on the result info  208 , which may include at least some of the image assessment information  118 . For instance, the image processing module  210  may perform operations to determine an identity of the person in the face image  114  as a result of the image assessment information  118  satisfying a first set of thresholds specified in the face image criteria  108 . The operations to determine the subject&#39;s identity may include comparing the biometric signature  212  with various previously-obtained stored biometric signatures  216  stored in data storage  214 . The stored biometric signatures  216  may have a corresponding identity of a person associated therewith in the data storage  214 . In some embodiments, an identity of a person may be a name or alphanumeric combination (e.g., username, employee identification number) associated with a person. An identity of a person may be information other than a name or alphanumeric combination in some embodiments. For example, an identity of a person may be information associated with a previous instance in which a face image  114  of a particular person was captured, such as information regarding time, date, location, etc. The system  100  may generate a unique alphanumeric value for the identity of the person and store the alphanumeric value in data storage  214 . 
     As a result of determining that the biometric signature  212  is sufficiently similar to a stored biometric signature  216 , the image processing module  210  may generate an output indicating the identity associated with the stored biometric signature  216 . The image assessment information  118  for each of the stored biometric signatures  216  may be stored in the data storage  214  in association with the corresponding stored biometric signature  216 . Moreover, each of the stored biometric signatures  216  may be associated with information indicating an identity of the person pictured in the corresponding face image  114  (if known) and may include information indicating a time and date on which the corresponding face image  114  was captured or stored. 
     The image processing module  210  may perform operations to update, in the data storage  214 , the stored biometric signature  216  depending on the result info  208 . In response to determining that the image assessment information  118  satisfies a second set of thresholds specified in the face image criteria  108 , the image processing module  210  may cause the biometric signature  212  to be stored in the data storage  214 . The image processing module  210  may associate the biometric signature  212  now stored in the data storage  214  with a subject&#39;s identity. A name or other alphanumeric identifier for a person may be associated with the biometric signature  212 . In some instances, another one of the stored biometric signatures  216  may be disassociated with the identity such that the biometric signature  212  replaces an older stored biometric signature  216 . In some instances, the biometric signature  212  may be merged with an identity such that a plurality of biometric signatures  216  are stored in the data storage  214  in association with a single identity. 
     The biometric signature  212  generated may include an array of values representative of a comparison between the face image  114  and a corresponding template stored in the memory  126 . The biometric signature  212  may, for instance, represent differences in Euclidean distance between points in the face image  114  and points in the blank template. The stored template may include a multidimensional vector in Euclidean space. The memory  126  may store a plurality of templates that correspond to difference poses for face images  114  obtained. The instructions  128  ( FIG. 1 ) may include instructions that cause the one or more processors  102  to implement an artificial intelligence model to generate the template. The artificial intelligence model may include deep neural network (DNN) models and principles in which multilayered neural networks learn to generate templates and/or biometric signatures based on stored data regarding templates and face images. The system  100  implements DNN technologies to identify relevant features in face images and generate corresponding templates and biometric signatures. The stored biometric signatures  216  may be generated in the same way as the biometric signature  212 . 
     In some embodiments, the image assessment module  204  and the image processing module  210  may be part of the same system  100  and even part of the same device in some implementations. In some embodiments, the image assessment module  204  may send the output  206  over the communication network  218  to the image processing module  210 . The network  218  may be a wide area network (e.g., internet) in which the image processing module  210  is remotely located to the image assessment module  204 . In some embodiments, the network  218  may be a local area network. 
     Facial recognition can be a computationally intensive process; however, determining which face images  114  to process has been a complex and difficult challenge. The image processing module  210  performs facial recognition processing on select face images  114  that satisfy the face image criteria  118 . This process of evaluating the face images  114  based on the set of factors identified herein helps to ensure that select face images  114  having characteristics suited to successfully recognize the identity of persons imaged are processed. Therefore, the image processing module  210  performs facial recognition processing on face images  114  most likely to achieve a worthwhile result, thereby improving the computational efficiency of the system  100 . 
       FIG. 3  shows an environment  300  in which image assessment information  118  is evaluated based on the face image criteria  108  according to one or more embodiments. The environment  300  shows operations performed by the image assessment module  204  in connection with the system  100 . Image assessment information  118  is generated based on a face image  114  received from the face detection module  202 . The image assessment information  118  includes a set of alphanumeric values  302  representative of various characteristics of the face image  114 . The image assessment information  118  may be formatted as an array, such as a one-dimensional array in which the alphanumeric entries are separated by delimiters (e.g., semi-colon, comma, tab). The image assessment information  118  may be formatted as a multi-dimensional array or table having multiple columns and rows. 
     The image assessment information  118  includes size information  304  representative of a size of the face image  114 . The size information  304  may specify a number of pixels contained in the face image  114 , the width of the face image  114  (e.g., in pixels), a height of the face image  114  (e.g., in pixels), or an amount of data storage occupied by the face image. For example, the size information  304  in the environment  300  indicates that the face image  114  has 50176 pixels. 
     The image assessment information  118  includes pose information  306  representative of a pose of a person&#39;s face or head in the face image  114 . The pose information  306  may indicate an orientation of the face relative to an optical axis of the camera  106  capturing the face image  114 . The image assessment module  204  may generate the pose information  306  based on the features identified in the person&#39;s face and head in the face image  114 . For instance, if the top of a person&#39;s head and a right eye of the subject are visible in the face image  114 , the pose information  306  may indicate that the head is oriented downward and to the left of the optical axis of the camera  106 . The pose information  306  may have a format indicating rotation about one or more axes. The pose information  306  shown, for example, indicates that the head in the face image  114  is rotated 10° about a vertical axis and 0° about a horizontal axis. In some embodiments, the pose information  306  may include alphanumeric values representative of various features that can be identified in the face image  114 , which may correspond to rotational states of the head shown in the face image  114 . 
     The image assessment information  118  also includes sharpness information  308  representative of the sharpness of the face image  114 . Sharpness reflects a rate or quickness of change between boundaries of color or tone in the face image  114 . The image assessment module  204  may generate the sharpness information  118  based on an operation involving a decimation operation, as described below. The sharpness information  308  may include an alphanumeric value indicating a sharpness level of the face image  114 . 
     The image assessment information  118  also includes contrast quality information  310 . Contrast quality is an assessment of the overall quality of contrast for the face image  114 . The image assessment module  204  may generate the contrast quality information  310  based on evaluations of contrast in certain areas of a face in the face image  114 . The contrast quality information  310  may include one or more alphanumeric values indicating the contrast quality of the face image  114 . 
     The image assessment module  204  determines whether to advance the face image  114  for facial recognition processing as a result of the comparison  120  between the image assessment information  118  and the face image criteria  108 . The face image criteria  108  may include one or more of size criteria  312 , pose criteria  314 , sharpness criteria  316 , and contrast quality criteria  318 . The face image criteria  108  may be formatted as a one-dimensional de-limiter separated array, or a multi-dimensional array, as described above with respect to the image assessment information  118 . The face image criteria  108  may include Boolean logic, conditions, and/or relationships regarding corresponding information in the image assessment information  118 . 
     In the environment  300 , the size criteria  312  specify one or more acceptable size ranges or thresholds for the size information  304  of the face image  114 . The size criteria  312 , for example, may specify a minimum width for the face image  114 , a minimum height for the face image  114 , and/or a minimum total pixel count for the face image  114 . The pose criteria  314  specify acceptable poses for the face imaged in the face image  114 , as indicated by the pose information  306 . By way of non-limiting example, the pose criteria  314  may include acceptable ranges for orientation of the head in the face image  114  or acceptable groups of facial features represented in the face image  114 . The sharpness criteria  316  specify one or more acceptable sharpness ranges or thresholds for the sharpness information  308  of the face image  114 . The contrast quality criteria  318  specify one or more acceptable contrast quality ranges or thresholds for the contrast quality  310  of the face image  114 . In some embodiments, some of the face image criteria  108  may be dependent upon the image assessment information  118 . For example, if the pose information  306  is within a first set of ranges, the face image criteria  108  may specify corresponding set of size criteria  312 , sharpness criteria  316 , and/or contrast quality criteria  318 . 
     The image assessment module  204  performs comparisons between the values  302  for the image assessment information  118  and corresponding criteria in the face image criteria  108 . As a result of determining that the image assessment information  118  satisfies the face image criteria  108 , the image assessment module  204  may generate the output  206  advancing the face image  114  for facial recognition processing. The output  206  may include an indication that the face image  114  is satisfactory and may include the face image  114  or a reference to a location where the face image  114  may be obtained. The output  206  may include the assessment result information  208 , which may include at least some of the image assessment information  118  or information related thereto. As a result of receiving the output  206 , the image processing module  210  may perform various facial recognition processing  122  using the face image  114 . 
     The output  206  generated may be based at least in part on the face image criteria  108 , which may include information, such as a classification, to be generated based on the values  302  of the image assessment information  118 . The face image criteria  108  may specify that the assessment output  206  should include a classifier corresponding to an operation to be performed if the values  302  fall within certain ranges. For instance, as a result of the values  302  falling within a first range specified in the face image criteria  108 , the image assessment module  204  would generate an output  206  that includes first information that would cause the image processing module  210  to attempt to recognize the face in the face image  114 . As a result of the values  302  falling within a second range specified in the face image criteria  108 , the image assessment module  204  would generate an output  206  that includes second information that would cause the image processing module  210  to attempt to store the biometric signature  212  for the face image  114  as a new stored biometric signature  216  for future facial recognition. 
       FIG. 4  shows an environment  400  in which the system  100  verifies consistency of the images  104  obtained according to one or more embodiments. The system  100  evaluates the images  104  obtained to help ensure that an inconsistent or unrepresentative image  104  is not used for facial recognition purposes. For instance, if there is a significant amount of motion during the image capture or there is cigarette smoke passing through the image, these occurrences may result in a biometric signature  212  that is not adequately representative of the subject. Therefore, using the biometric signature  212  in such a situation could lead to inaccurate facial recognition results and waste resources. The system  100  verifies consistency of images obtained before proceeding to perform facial recognition processing  122 . Aspects of the environment  400  may be performed by the image assessment module  204  and/or the image processing module  210 . 
     The environment  400  involves obtaining a plurality of images  105  sequentially captured by the camera  106  over time, as described herein with respect to  FIG. 1 . The camera  106  may capture at least a first image  104   a  and a second image  104   b  sequential to the first image  104   a  for consistency verification. The plurality of images  105  obtained are not necessarily immediately sequential—for example, a time period (e.g., 10 milliseconds, 1 second, 2 seconds) may separate first time at which the first image  104   a  is captured and a second time at which the second image  104   b  is captured. In some instances, there may be one or more images captured between the first image  104   a  and the second image  104   b.    
     One of the images  104   a  and  104   b  may contain the face image  114  that the system  100  is evaluating for facial recognition processing  122 . Although only two images  104   a  and  104   b  are depicted and described, the system  100  may evaluate more than two images  104  in connection with image consistency verification. In some embodiments, the signatures  402  obtained may be of particular portions or subimages of the images  104 . For instance, the signature  402   a  may be of a portion of the image  104   a  detected as being a face image  114  and the signature  402   b  may be of a corresponding portion of the image  104   b . In some embodiments, the signature  402   a  may be the biometric signature  212  of the face image  114  of a face captured at a first time, and the signature  402   b  may be a biometric signature of the face image  114  of the face captured at a second time after the first time. Signatures  402  are generated that each corresponds to one of the images  104 . Generation of the signatures  402   a  and  402   b  may be performed according to the process discussed for generating biometric signatures  212  herein. 
     The system  100  may perform a comparison  404  between the first signature  402   a  and the second signature  402   b  to determine consistency between the signatures. The signatures  402   a  and  402   b  may be represented as respective arrays of values corresponding to multidimensional vectors in Euclidean space. The system  100  may determine differentials between corresponding values in arrays to the signatures  402   a  and  402   b  to determine how consistent the images  104   a  and  104   b  are with each other. The system  100  may compare  404  the differentials with one or more thresholds or ranges and determine that the images  104   a  and  104   b  are within an acceptable range of consistency. If the signatures  402   a  and  402   b  are determined to be sufficiently similar or close in vector space, an output  408  is generated indicating that the images  104   a  and  104   b  are verified as consistent. If the signatures  402   a  and  402   b  are insufficiently similar, the system  100  may not generate the output  408  or generate an output disapproving facial recognition processing  122  for the face image  114 . In some embodiments, the system  100  may consider more than two sequential images  104  or subimages in the consistency verification of the environment  400 . For example, the system  100  may verify that three signatures  402  corresponding to three sequential images  104  are sufficiently similar as a condition precedent to issuing the output  408 . 
     In some embodiments, the system  100  may perform the facial recognition processing  122  as a result of generating both the output  408  verifying image consistency and the output  206  verifying that the image assessment information  118  satisfies the face image criteria  108  set of performing facial recognition processing  122 . Once the consistency of the images  104  and the quality of the face image  114  are verified, the system  100  may perform the facial recognition processing  122  in accordance with the output  206 , as discussed herein. The system  100  may, for example, determine an identity of the person depicted in the face image  114  or store the face image  114  in the data storage  214  as one of the stored biometric signatures  216 . The efficiency and reliability of the system  100  for performing facial recognition processing  122  may be improved by processing certain face images  114  of sufficiently high quality and consistency. 
       FIG. 5  shows a diagram  500  of a plurality of regions of the face image  114  according to one or more embodiments. The diagram  500  is generated by the system in connection with generating the contrast quality information  310 . The system  100  identifies a plurality of particular features of the face in the face image  114  and determines a plurality of regions in the face image  114  respectively centered about the particular features of the face in the face image  114 . The system  100  may generate or identify, for each region, a vertical axis  501  and a horizontal axis  503  that are each centered within a region. 
     The system  100  identifies pupil centers of the left and right eyes in the face image  114  and determines a first region  502  centered on a left pupil  505  and a second region  504  centered on a right pupil  507 . The system  100  also identifies a left mouth corner  509  and a right mouth corner  511  in the face image  114  and determines a third region  506  centered around the left mouth corner  509  and a fourth region  508  centered on the right mouth corner  511 . The system  100  may also determine a fifth region  510  between the first region  502  and the second region  504  and a sixth region  512  between the third region  506  and the fourth region  508 . The system  100  may further determine a seventh region  514  between the fifth region  510  and the sixth region  512 . The system  100  may also determine a first horizontal line  516  extending between the vertical axis  501  centered on the left pupil and the vertical axis  501  centered on the right pupil. The system  100  may also determine a second horizontal line  518  extending between the vertical axis  501  centered on the left mouth corner  507  and the vertical axis  501  centered on the right mouth corner  509 . In some instances, features in the face image  114 , such as the left and right pupils or the left and right mouth corners, may not be horizontally aligned (i.e., located at different vertical positions). In such instances, the first horizontal line  516  or the second horizontal line  518  may be positioned halfway between the offset facial features. 
     The sizes of each of the plurality of regions may be based on distances between the particular features of the face image  114  identified. A width of at least some of the regions may be based on a first distance d 1  between the left pupil center and the right pupil center. The first distance d 1  may be a distance in a horizontal direction parallel to horizontal axis of the face image  114 . A width of at least some of the regions may be based on a second distance d 2  between the left mouth corner and the right mouth corner. The second distance d 2  may be a distance in a horizontal direction parallel to the horizontal axis of the face image  114 . A height of at least some of the regions may be based on a third distance d 3  between the mouth and the eyes. The distance d 3  may be a distance between a center horizontal axis of the fifth region  510  and a center horizontal axis of the sixth region  512 . 
     In some instances, there may be more or fewer than seven regions. For instance, if a single eye is not visible in the face image  114 , the system  100  may decide not to determine the region corresponding to the other non-visible eye. As a result, the sizes of the regions may be determined in a different manner. If the right eye is not visible, for example, a horizontal size of the first region  502  may be determined as the distance between the center of the left pupil to a bridge portion of the nose in the face image  114 . As a result of determining the plurality of regions, the system  100  may use the plurality of regions to generate the contrast quality information  310 . 
       FIG. 6  shows a method  600  for evaluating a plurality of images for facial recognition processing according to one or more embodiments. The method  600  is performed by one or more components of the system  100 , such as the image assessment module  204  and the face detection module  202 . The method  600  begins by receiving  602  a plurality of images  105 . The plurality of images  105  are generated by the camera  106  and may be received therefrom or over a network. Next, the system  100  detects  604  faces in one or more of the images  104 . For example, the system  100  may detect, in the one or more images, a set of facial features qualifying as a face image  114 . Detecting  604  the faces may include, for each image  104  detected as including a face image  114 , identifying a region in the image  104  containing the face and generating information useable to process the face image  114 . The system  100  may identify a set of coordinates corresponding to the region containing the face image  114 , or extract information representative of the face image  114  for further processing. If no faces are detected in the plurality of images  105 , the system  100  may wait to receive  602  another plurality of images  105 . 
     The system  100  then proceeds to generate  606  assessment information  118  for each face image  114  detected in the plurality of images  105 . Generating  606  the assessment information  118  includes generating the size information  304 , the pose information  306 , the sharpness information  308 , and the contrast quality information  310 , as described herein. 
     The method  600 , in some embodiments, may include determining  607  whether the images  104  received are consistent. For instance, the system  100  may generate a biometric signature  212  of the face image  114  in a first image  104  of the plurality of images  105  received and generate a biometric signature  212  of the face image  114  in a second image  104  that is sequential to the first image  104  in the plurality of images  105 . The system  100  may compare the first and second biometric signatures  212  to determine whether they are sufficiently similar. If so, the method  600  may proceed; otherwise, the method  600  may return to a previous operation, such as by obtaining  612  a next face image  114 . In some embodiments, the system  100  may determine consistency of a first image  104  and a second image  104  sequential to the first image  104  in the plurality of images  105  received prior to generating  606  assessment information  118  for face images  114  detected. In some embodiments, determining  607  whether the face images  114  are consistent may not be performed. Further description of the determination  607  is described below with respect to  FIG. 12  and elsewhere herein. 
     Thereafter, the assessment information  118  is compared with the face image criteria  108  and the system  100  determines  608  whether the assessment information  118  satisfies the face image criteria  108 . Determining  608  whether the assessment information  118  satisfies the face image criteria  108  may include one or more of a comparison of the size information  304  with the size criteria  312 , a comparison of the pose information  306  with the pose criteria  314 , a comparison of the sharpness information  308  with the sharpness criteria  316 , and a comparison of the contrast quality information  310  with the contrast quality criteria  318 . 
     If, on the other hand, it is determined that the assessment information  118  does not satisfy the face image criteria  108 , the method  600  proceeds by obtaining  612  the next face image  114  for evaluation. If no more face images  114  remain for processing, the system  100  waits to receive further images  104  for evaluation. 
     As a result of determining  608  that the assessment information  118  satisfies the face image criteria  108 , the method proceeds by advancing  610  the face image  114  corresponding to the assessment information  118  for facial recognition processing. By determining  608  whether the assessment information  118  satisfies the face image criteria  108  before performing facial recognition processing, the system  100  obviates unnecessary expenditure of potentially expensive computational resources, storage, or network bandwidth on face images  114  that have undesirable characteristics for facial recognition processing. Advancing  610  the face image  114  for facial recognition processing causes the system  100  to compare the biometric signature  212  of the face image  114  with stored biometric signatures  216  in the data storage  214 . For instance, facial recognition processing may include determining whether the biometric signature  212  is a match for a stored biometric signature or whether the biometric signature  212  corresponds to a new identity that the system  100  should learn. Further description of facial recognition processing is described herein with respect to  FIG. 11  and elsewhere herein. The system  100  may return information indicating an identity of a person depicted in the face image  114  evaluated as a result of the face image processing. 
       FIG. 7  shows a method  700  involved in generating the sharpness information  308  according to one or more embodiments. The method  700  may be performed by the image assessment module  204  of the system  100 . The method  700  is performed in connection with generating  606  the assessment information  118  of the method  600  and as described elsewhere herein. The method  700  begins by obtaining a face image  114  from an image  104 , which may involve extracting or copying information representative of a region detected as including a face in the image  104 . The face image  114  obtained  702  is stored in memory of the system  100  (e.g., random-access memory) for sharpness evaluation. 
     Next, the method  700  includes determining  704  edge information of the face image  114  obtained. Edge information indicates a sharpness of transition at boundaries between different colors, tones, etc., in the face image  114 . The edge information may include an array containing numerical values representative of differences in color, tone, etc., between adjacent or nearby pixels of the face image  114 . The array may be a one-dimensional array or multi-dimensional array including entries separated by delimiters (e.g., semi-colon, tab, comma). Higher numerical values may indicate a sharper transition in characteristics of adjacent or nearby pixels whereas a lower numerical value may indicate a relatively less sharp transition. The edge information may be generated, for example, by processes including performing a Fourier transform (e.g., Fast Fourier Transform, Discrete Fourier Transform) on the face image, application of a Sobel filter, or any such appropriate an edge detection algorithm. 
     The method  700  includes downsampling  706  the face image  114  to generate a downsampled version of the face image  114 . Downsampling  706  may involve taking a first pixel from the face image  114  and then every Nth pixel (where N is an integer value) thereafter until there are no more pixels remaining to sample in the face image  114 . One example of downsampling  706  is by performing a decimation operation in which the sample rate of the face image  114  is reduced by an integer factor. Downsampling  706  may also include application of a finite impulse response filter or Chebyshev filter on the face image  114 . As a result of downsampling  706  the face image  114 , a downsampled face image is obtained. At  708 , the method  700  includes determining edge information for the downsampled face image in a manner similar to determining  704  the edge information of the face image  114 . 
     Next, the method  700  includes determining  710  a sharpness differential between edge information for the face image  114  and the edge information for the decimated face image. Determining  710  the sharpness differential may include determining differences between values in the edge information for the face image  114  and corresponding values in the downsampled face image. The system  100  may determine a maximum or average value for a portion of the edge information corresponding to a particular subregion of the face image  114 , and compare that value with a portion of edge information of a corresponding subregion of the decimated face image. The system  100  may compute edge information differences for subregions of the entire face image  114  and generate sharpness information corresponding to the differences in the edge information. Larger values for the sharpness information  308  may indicate that the face image  114  has relatively high sharpness, which is favorable for proper evaluation of a biometric signature  212  of the face image  114 . Conversely, lower values for the sharpness information  308  may indicate that the face image  114  has relatively lower sharpness, which is unfavorable for proper evaluation of the face image  114 . 
       FIG. 8  shows a method  800  involved in generating contrast quality information  310  according to one or more embodiments. The method  800  may be performed by the image assessment module  204 . The method  800  is performed in connection with generating  606  the assessment information  118  of the method  600  and as described elsewhere herein. The method  800  begins by identifying  802  facial features in the face image  114  being evaluated. The system  100  analyzes the face image  114  and identifies which, if any, of a set of defined facial features are present in the face image  114 . The system  100  may identify regions, e.g., by pixel coordinates or locations, in the face image  114  that correspond to the facial features identified. The system  100  may then generate information identifying the types of facial feature (e.g., eye, nose, mouth) and the region corresponding to each facial feature. The information regarding the facial features may identify locations of particular portions of the facial features, such as locations of pupil centers and locations of mouth corners. 
     Next, the system determines  804  distances between the facial features identified in  802 . Determining  804  the distances may involve determining the distance d 1  between pupil centers in the face image  114  and a distance d 2  between corners of the mouth, as shown in  FIG. 5 . The distances d 1  and d 2  may be distances in directions parallel to a horizontal axis of the face image  114 . That is, the distance d 1  may be a distance between a vertical axis extending vertically through the left pupil center in the face image  114  and a vertical axis extending vertically through the right pupil center in the face image  114 . The distance d 2  may be a distance between a vertical axis extending vertically through the left mouth corner in the face image  114  and a vertical axis extending vertically through the right mouth corner in the face image  114 . Determining  804  distances between facial features may also include determining a distance d 3  between the eyes and the mouth in the face image  114 . In particular, the distance d 3  is determined between the first horizontal line  516  extending between the eye regions and the second horizontal line  518  extending between the mouth corner regions. 
     The method  800  then proceeds to determining  806  dimensions of regions that are to be allocated for and relative to the facial features of the face image  114 . In particular, the system  100  determines dimensions of the regions  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514  (collectively “the regions”). The regions to be determined are rectangular in shape, and dimensions of each of the regions are calculated based on the distances d 1 , d 2 , and d 3 . Each of the first region  502 , the second region  504 , the fifth region  510 , and the seventh region  514  may have a width (i.e., in the horizontal direction) equal to half the distance d 1 —that is a width of (d 1 )/2. Each of the third region  506 , the fourth region  508 , and the sixth region  512  may have a width equal to half the distance d 2 —that is, a width of (d 2 )/2. Each of the regions  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514  has a height (i.e., in the vertical direction) equal to half the distance d 3 —that is, a height of (d 3 )/2. In some instances, however, the dimensions of some or all of the regions may be different. For example, for a face image  114  in which only a single mouth corner or single pupil is visible, the width of some regions may be based on distances to other facial features, such as a bridge of the nose. 
     At  808  of the method  800 , the system allocates  808  the regions in the face image  114  around or relative to facial features. The system  100  allocates the first region  502  centered on the left pupil center, the second region  504  centered on the right pupil center, the third region  506  on the left mouth corner, and the fourth region  508  at the right mouth corner. The fifth region  510  is allocated to have a vertical center axis  520  thereof vertically centered along the first horizontal line  516  with the right edge of the fifth region  510  positioned at or on the left edge of the first region  502  and the left edge of the fifth region  510  positioned at or on the right edge of the second region  504 . The sixth region  512  is allocated to have a vertical center axis  522  thereof vertically centered along the second horizontal line  518  with the right edge of the sixth region  512  positioned at or on the left edge of the third region  506  and the left edge of the sixth region  512  positioned at or on the right edge of the fourth region  508 . The seventh region  514  is allocated to be positioned between the fifth region  510  and the sixth region  512  with an upper edge positioned at or on the lower edge of the fifth region  510  and a lower edge positioned at or on the upper edge of the sixth region  512 . The seventh region  514  is further allocated to have a vertical center axis  501  positioned halfway, in the horizontal direction, between the vertical axis  501  of the fifth region  510  and the vertical axis  501  of the sixth region  512 . At the conclusion of the method  800 , the system  100  proceeds to determine the contrast quality information  310  for the regions. 
       FIG. 9  shows a method  900  for determining the contrast quality information  310  for the regions allocated in the method  800 . The method  900  may be performed by the image assessment module  204 . The method  900  is performed in connection with generating  606  the assessment information  118  of the method  600  and as described elsewhere herein. The method  900  begins by computing  902  maximum differences between pixel values for pixels in each of the regions  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514 . The pixel value for a pixel corresponds to an alphanumeric value representing the brightness of the pixel. The maximum difference between pixel values is computed by obtaining, for each region, a minimum pixel value and a maximum pixel value and determining the difference between the maximum pixel value and the minimum pixel value. 
     The method  900  proceeds by discarding  904  the maximum pixel difference for one or more regions. In particular, the system  100  may include a defined number of regions for which to determine the contrast quality information  310 . In some embodiments, the defined number of regions may be five. The system  100  discards  904  the maximum pixel differences for the regions having the lowest pixel differences to reach the defined number. In this example, the system  100  discards the lowest two maximum pixel differences to reach five maximum pixel differences. In some instances, the defined number of maximum pixel differences to discard may change according to other information. For instance, if the pose information  306  indicates that the face in the face image  114  has certain orientations (e.g., face is looking 45° to one side), the number of maximum pixel differences to discard may be fewer than five. 
     The method  900  includes computing  906  an average pixel difference of the remaining regions. That is, the system  100  computes an average maximum pixel difference for the regions  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514  that were not discarded in  904 . Computing  906  the average pixel difference may include computing a sum of the maximum pixel differences for the regions  502 ,  504 ,  506 ,  508 ,  510 ,  512 , and  514  that were not discarded in  904 . 
     The method  900  may then proceed to normalizing  908  the average maximum pixel difference computed in  906 . Normalizing  908  may involve determining into which of a plurality of ranges the average maximum pixel difference falls, and calculating a corresponding normalized value based on the range determined.  FIG. 13  shows a graph representing ranges for the calculated average maximum pixel difference values and corresponding normalized values on a 0.0 to 1.0 scale. The system  100  determines which range the calculated value (CV) falls into from less than T 1 , T 1  to T 2 , T 2  to T 3 , or greater than T 3 . Subsequent to determining into which range the calculated value falls, the system may calculate a corresponding normalized value (NV) using a formula for the range. The formula for each range may be as follows: 
       If CV&lt; T 1, then NV=CV/( T 1×0.2);
 
       If  T 1≤CV&lt; T 2, then NV=0.2+(CV− T 1)/( T 2 −T 1)×0.4;
 
       If CV≥ T 2, then NV=0.6+(CV− T 2)/( T 3 −T 2)×0.4
 
     The resulting normalized value (NV) is a unitless value from 0.0 to 1.0. The normalized average maximum pixel difference value and/or the calculated average maximum pixel difference value may be included in the assessment information  118 . In some embodiments, the system  100  may determine a category or classification for the calculated average maximum pixel difference value instead of determining a normalized average maximum pixel difference value. For example, the system  100  may classify a calculated average maximum pixel difference value between T 1  and T 2  as being sufficient to recognize an identity of a person in a face image  114  and classify a calculated average maximum pixel difference value greater than T 2  as being sufficient to learn or update an identity of the person in the face image  114 . As a result of normalizing  908  the calculated value, the method  900  includes providing  910  contrast quality information  310  corresponding to the average pixel difference value computed. 
       FIG. 10  shows a method  1000  for performing facial recognition processing according to one or more embodiments. The method  1000  may be performed by one or more appropriate components of the system  100 , such as the image processing module  210 . The method  1000  is performed in connection with advancing  610  the face image  114  for facial recognition processing of the method  600  and as described elsewhere herein. The method  1000  begins by receiving  1002  or otherwise obtaining the assessment output  206  indicating result information  208  regarding the assessment performed. The result information  208  may include the assessment information  118  or otherwise indicate that certain facial recognition processing operations are to be performed. Receiving  1002  the result information  208  may include receiving the face image  114  or a reference thereto. 
     The method  1000  includes determining  1004  whether to update an existing identity or learn a new identity based on the result information  208 . The result information  208  may indicate that the image assessment information  118  satisfies criteria for learning or updating an identity according to the face image criteria  108 . In some embodiments, the result information  208  specifies which of the face image criteria  108  the assessment information  118  satisfies and the system  100  may determine whether to update or learn an identity based on the criteria specified. In some embodiments, the result information  208  may include instructions instructing to update an identity or learn a new identity. In some embodiments, the result information  208  may include some or all of the assessment information  118 , which is used to determine whether to update or learn an identity based on the face image criteria  108 . If it is determined to update or learn the identity in  1004 , the method  1000  proceeds to update  1006  the data storage  214  as described with respect to  FIG. 11  below and elsewhere herein. 
     If the system  100  determines  1004  not to update or learn an identity, e.g., by virtue of the assessment information  118  failing to satisfy an appropriate set of criteria, the system  100  proceeds to determine  1008  whether to identify the person depicted in the face image  114  based on the result information  208 . The result information  208  may indicate that the image assessment information  118  satisfies criteria identifying a person according to the face image criteria  108 . In some embodiments, the result information  208  specifies which of the face image criteria  108  the assessment information  118  satisfies and the system  100  may determine to identify the person depicted in the face image  114  based on the criteria specified. In some embodiments, the result information  208  may include instructions instructing the system  100  to identify the person depicted in the face image  114 . In some embodiments, the result information  208  may include some or all of the assessment information  118 , which is used to determine  1008  whether to identify the person based on the face image criteria  108 . 
     If it is determined in  1008  to identify the person depicted in the face image  114 , the method  1000  proceeds to generate  1010  a biometric signature  212  of the face image  114 . Then, the system  100  compares  1012  the biometric signature  212  generated to the stored biometric signatures  216  in the data storage  214  to determine a match. If the biometric signature  212  generated is sufficiently similar to one of the stored biometric signatures  216 , the system  100  returns information indicating the identity of the person depicted in the face image  114 . The process of comparing  1012  the biometric signature  212  to the stored biometric signatures  216  is potentially time and resource intensive. By comparing those face images  114  having attributes matching the face image criteria  108 , the rate of false positives is reduced and the efficiency of the system  100  is improved. That is, the resources of the system  100  are directed to performing facial recognition processes on select face images  114  having high resolution, sharpness, and contrast quality, and those representing face poses likely to produce a positive identification. If, in  1008 , the system  100  determines not to identify  1008  the person in the face image  114 , the face image  114  is discarded  1014  (e.g., not stored in memory), and the system  100  proceeds to obtain  612  the next face image  114  in the method  600  or proceeds to another set of operations defined herein. 
       FIG. 11  shows a method  1100  of updating data storage according to one or more embodiments. The method  1100  is performed as part of or in connection with updating  1006  data storage in the method  1000 . The method  1100  may be performed by one or more appropriate components of the system  100 , such as the image processing module  210 . The method  1100  is performed as a result of the system  100  determining that the face image  114  has characteristics sufficient to satisfy a set of criteria for learning, updating, or merging identities. The set of criteria for performing the method  1100  may be different than the criteria for determining whether to identify a person in a face image  114 . It may be desirable to use high quality face images  114  as a basis for determining which face images  114  are suitable to use as a reference for identifying persons, so the set of criteria for performing the method  1100  may be higher than the set of criteria for determining whether to identify a person in  1008  of  FIG. 10 . 
     The method  1100  begins by generating  1102  a biometric signature  212  of the face image  114 . Then, the biometric signature  212  generated is compared  1102  with the stored biometric signatures  216  to determine a match. If the system  100  determines  1104  that there is no match between the biometric signature  212  and one of the stored biometric signatures  216  (No at  1106 ), the method  1100  may proceed to generate  1108  a new identity for the biometric signature  212  generated in  1102 . This may include obtaining identifying information regarding the person depicted, such as name, age, identification numbers. In some embodiments, the information regarding the identity of the person may be obtained via user input. Thereafter, the method  1100  may terminate and the system  100  may return to another operation described herein, such as obtaining  612  the next face image  114  to be evaluated in the method  600 . 
     If the system  100  determines  1104  that a match exists between the biometric signature  212  and one of the stored biometric signatures  216  (Yes at  1106 ), the method  1100  next proceeds to determine whether the biometric signature  212  is a match to multiple identities. For instance, the system  100  may determine that the biometric signature  212  is sufficiently similar to a plurality of stored biometric signatures  216  each corresponding to a different identity or person. In such an event, the method  1100  may proceed to merge  1112  the plurality of identities. Merging  1112  the identities may include evaluating the assessment information  118  associated with the face image  114  of the biometric signature  212  under consideration. The system  100  may determine whether the assessment information  118  satisfies a set of criteria for merging identities as a condition precedent to merging  1112 . The set of criteria for merging  1112  identities may include different criteria than the set of criteria for updating  1004  or identifying  1008  discussed with respect to the method  1000 —for example, the set of criteria for merging  1112  may include higher thresholds for the size information  304 , the pose information  306 , the sharpness information  308 , or the contrast quality information  310 . In some embodiments, the system  100  may not merge  1112  the identities if information stored in association with the stored biometric signatures  216  if there are inconsistencies associated with the identities. If, for example, the identities have different social security numbers associated or the there is an indication that a twin exists for the person, the system  100  may not merge the identities. 
     Next, the method  1100  proceeds by determining  1114  whether the assessment information  112  represents an improvement over the assessment information  118  of the stored biometric signature  216  matched with the biometric signature  212 . If so, the method  1100  proceeds to replace  1116  the corresponding stored biometric signature  216  with the current biometric signature  212  and the associated assessment information  118 . This will ensure that the biometric signature  212  used as a basis for determining the identity of a person is of high quality. 
     At  1118 , the method  1100  involves determining whether the stored biometric signature  216  for which a match was determined exceeds a defined time threshold. In particular, the system  100  determines a time period between a current time and date and the time and date at which the stored biometric signature  216  was captured. If the time period exceeds a defined time period threshold, the stored biometric signature  216  is replaced  1116  with the current biometric signature  212 . The defined time period threshold is an alphanumeric value stored in memory that indicates a time period after which the stored biometric signature  216  should be replaced. This ensures that the stored biometric signatures  216  are recent so that old face images  114  are not used as a basis for facial recognition processing. If the time period for the stored biometric signature  216  does not exceed the time period threshold, the method  1100  proceeds to provide  1120  identity information regarding the identity of the person with whom the biometric signature  212  was matched in  1106 . 
       FIG. 12  shows a method  1200  for verifying the consistency of the images  104  being evaluated or subimages thereof. The method  1200  is performed as part of or in connection with  607  of the method  600 . The method  1200  may be performed by one or more appropriate components of the system  100 , such as the image assessment module  204 . The method  1200  begins by receiving  1202  a first image captured by the camera  106 . In some embodiments, the first image may be a first image  104  of the plurality of images  105  that contains one or more face images  114  to be evaluated. In some embodiments, the first image may be the face image  114  currently being evaluated in an image  104  of the plurality of images  105 . Next, the method  1200  includes generating  1204  a first signature of the first image. For instance, the system  100  may generate a biometric signature  212  of the face image  114  being evaluated. 
     The method  1200  includes receiving  1206  a second image captured by the camera  106 . In some embodiments, the second image is an image  104  sequential to the first image in the plurality of images  105 . In some embodiments, the second image is the face image  114  of the same person captured in an image  104  sequential to the first image in the plurality of images  105 . Thereafter, the method  1200  includes generating  1208  a second signature of the second image received in  1206 —for example, generating a biometric signature of the face image  114  corresponding to the second image. 
     The method  1200  further includes comparing  1210  the first signature and the second signature to determine a degree of similarity or correspondence therebetween. For instance, the system  100  may compare vectors and points between the first and second signatures to determine how similar they are. The system  100  may generate a set of numerical values indicating differences between the first and second signatures. At  1212 , the method  1200  involves determining whether the first and second images are consistent based on the comparison of the signatures in  1210 . The determination  1212  of whether the first image and the second image are consistent may be based on a comparison to a threshold or range for acceptable consistency or similarity. 
     If the system  100  determines that a result of the comparison  1210  indicates sufficient similarity between the first image and the second image, the method  1200  proceeds to authorize  1214  facial recognition processing. For instance, the system  100  may generate result information  208  indicating that the image being evaluated is consistent or stable. If, on the other hand, it is determined that the result of the comparison  1210  indicates insufficient similarity, the method  1200  proceeds to deny  1216  facial recognition processing for the image  104  or face image  114  being considered for evaluation. 
     Consistency, as discussed herein, indicates that sequential images  104  are stable such that the image  104  or face image  114  being evaluated is undistorted or not blurred. Inconsistent images  104  or face images  114  may capture subjects who are in motion or in which other subject matter may disrupt image evaluation. For instance, cigarette smoke in an image may distort a face image  114  captured such that it has difficult features to discern. As another example, a person running or in the process of turning may have blurred features if captured during motion. Verifying the consistency of the images helps to ensure that resources of the system  100  are not wasted on processing blurred, moving, or otherwise suboptimal images. 
     The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments. 
     These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.