Patent Publication Number: US-2023147924-A1

Title: Image processing system, imaging system, image processing method, and non-transitory computer-readable medium

Description:
TECHNICAL FIELD 
     This disclosure relates to an image processing system, an imaging system, an image processing method, and a non-transitory computer-readable medium. 
     BACKGROUND ART 
     Biometric authentication (iris authentication) using an iris included in an eye has been known. In the biometric authentication, a feature value of an image region that represents an iris of a target person being included in a captured image of the target person is extracted, and the extracted feature value is registered in a database. For example, Patent Literature 1 discloses an iris authentication system that selects a feature value having a relatively high degree of authentication performance from a plurality of iris images being captured under different illumination conditions by using a camera, and registers the feature value. 
     CITATION LIST 
     Patent Literature 
     
         
         [Patent Literature 1] International Patent Publication No. WO2005/109344 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     This disclosure is to improve the technique described above. 
     Solution to Problem 
     An image processing system according to a first aspect of this disclosure includes a detection unit, an identification unit, and a feature extraction unit. The detection unit detects, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value. The identification unit identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. The feature extraction unit extracts a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     An imaging system according to a second aspect of this disclosure includes an imaging device, and an image processing device. The imaging device captures an image of a target person, and generates a first image. The image processing device includes: a detection unit configured to detect, from the first image, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value; an identification unit configured to identify, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value; and a feature extraction unit configured to extract a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     An image processing method according to a third aspect of this disclosure includes a detection step, an identification step, and a feature extraction step. The detection step detects, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value. The identification step identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. The feature extraction step extracts a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     A non-transitory computer-readable medium according to a fourth aspect of this disclosure stores an image processing program causing a computer to execute an image processing method including a detection step, an identification step, and a feature extraction step. The detection step detects, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value. The identification step identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. The feature extraction step extracts a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram illustrating a configuration of an image processing system according to a first example embodiment; 
         FIG.  2    is a flowchart illustrating processing of the image processing system according to the first example embodiment; 
         FIG.  3    is a schematic configuration diagram of an imaging system according to a second example embodiment; 
         FIG.  4    is a block diagram illustrating a configuration of the imaging system according to the second example embodiment; 
         FIG.  5    is a flowchart illustrating processing of the imaging system according to the second example embodiment; 
         FIG.  6    is a flowchart illustrating registration processing of an image processing device according to the second example embodiment; 
         FIG.  7    is a diagram for describing one example of display in registration processing of an image processing device according to a third example embodiment; 
         FIG.  8    is a diagram for describing one example of display in the registration processing of the image processing device according to the third example embodiment; 
         FIG.  9    is a block diagram illustrating a configuration of an imaging system according to a fourth example embodiment; 
         FIG.  10    is a flowchart illustrating second evaluation value calculation processing of an image processing device according to the fourth example embodiment; 
         FIG.  11    is a diagram for describing the second evaluation value calculation processing of the image processing device according to the fourth example embodiment; 
         FIG.  12    is a schematic configuration diagram of an imaging system according to a fifth example embodiment; 
         FIG.  13    is a block diagram illustrating a configuration of the imaging system according to the fifth example embodiment; 
         FIG.  14    is a flowchart illustrating second evaluation value calculation processing of an image processing device according to the fifth example embodiment; 
         FIG.  15    is a diagram for describing the second evaluation value calculation processing of the image processing device according to the fifth example embodiment; 
         FIG.  16    is a diagram for describing a reflection pattern with respect to an irradiation pattern of light of a light source array according to the fifth example embodiment; 
         FIG.  17    is a diagram for describing a reflection pattern with respect to an irradiation pattern of light of the light source array according to the fifth example embodiment; 
         FIG.  18    is a schematic configuration diagram of an imaging system according to a sixth example embodiment; 
         FIG.  19    is a block diagram illustrating a configuration of the imaging system according to the sixth example embodiment; 
         FIG.  20    is a flowchart illustrating processing of the imaging system according to the sixth example embodiment; and 
         FIG.  21    is a configuration diagram of a computer according to the first to sixth example embodiments. 
     
    
    
     EXAMPLE EMBODIMENT 
     This disclosure will be described below with reference to example embodiments, but the disclosure in the claims is not limited to the example embodiments below. Further, all configurations described in the example embodiments are not necessarily essential as a means for solving the problem. For clarification of the description, the description and the drawings below are appropriately omitted and simplified. Note that, in each of the drawings, the same elements will be denoted by the same reference signs, and duplicate description will be omitted as necessary. 
     FIRST EXAMPLE EMBODIMENT 
     First, a first example embodiment according to this disclosure will be described by using  FIGS.  1  to  2   .  FIG.  1    is a block diagram illustrating a configuration of an image processing system  10  according to the first example embodiment. The image processing system  10  includes a detection unit  102 , an identification unit  104 , and a feature extraction unit  107 . 
     The detection unit  102  detects a candidate region from a first image in which a target person is captured, based on a first evaluation value. Here, the first evaluation value indicates a likelihood of an eye, and is calculated for an image region being set based on the first image. Further, the candidate region is an image region being estimated to represent the eye of the target person. 
     The identification unit  104  identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. Here, the second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. Further, the eye region is an image region that represents the eye. 
     The feature extraction unit  107  extracts a feature value of the identified eye region. 
     Next, an image processing method of the image processing system  10  will be described by using  FIG.  2   .  FIG.  2    is a flowchart illustrating processing of the image processing system  10  according to the first example embodiment. 
     Next, in step S 10 , the detection unit  102  detects a candidate region from a first image in which a target person is captured, based on a first evaluation value. 
     Next, in step S 12 , the identification unit  104  identifies an eye region from the detected candidate region, based on a second evaluation value. 
     Then, in step S 14 , the feature extraction unit  107  extracts a feature value of the identified eye region. 
     Here, in the method described in Patent Literature 1 described above, authentication performance of a feature value being registered in a database is determined to be relatively excellent between feature values being extracted from a plurality of captured images. Then, when imaging is performed under a condition such as a long distance between a target person and a camera, a wide capturing visual field of a camera, or a target person who is moving, an acquired captured image may include many portions other than an eye. In such a case, in the method described in Patent Literature 1 described above, authentication performance of a feature value to be registered may decrease. 
     However, according to the configuration of the present first example embodiment, the image processing system  10  identifies an eye region, based on different evaluation values at two stages indicating a likelihood of an eye. Therefore, even when an image region that represents a portion other than an eye is included in a first image being captured, extraction of a false feature value and registration can be avoided. In this way, authentication performance when authentication processing is performed by using a registered feature value can be improved. 
     SECOND EXAMPLE EMBODIMENT 
     Next, a second example embodiment according to this disclosure will be described by using  FIGS.  3  to  6   .  FIG.  3    is a schematic configuration diagram of an imaging system  2  according to the second example embodiment. The imaging system  2  is a computer system for performing biometric authentication, and captures a region of interest including an eye of a target person P, extracts a feature value being biometric authentication information included in the captured image, and registers the extracted feature value. Here, the biometric authentication is iris authentication in the present second example embodiment. The imaging system  2  includes an imaging device  20 , an image processing system (hereinafter referred to as an image processing device)  12 , and a database  30 . 
     The imaging device  20  is a computer that captures the target person P and generates a first image I 1  being a captured image, or the like. The imaging device  20  includes an imaging instrument  200  and a control unit  206 . 
     The imaging instrument  200  is a camera for capturing an eye, particularly, an iris of the target person P, and generating the first image I 1 . The imaging instrument  200  includes an imaging element such as a charge coupled device (CCD) and a complementary metal oxide semiconductor (CMOS) element. An imaging surface of the imaging instrument  200  includes a pixel array in which a photoelectric transducer (not illustrated) that converts an optical image of the target person P into an electric signal is two-dimensionally arrayed. In the present second example embodiment, the imaging instrument  200  may be formed of a general-purpose camera of 12 M pixels (horizontal 4000 pixels, vertical 3000 pixels) and 60 fps, which is becoming a diffused product, such as an industrial camera. 
     The imaging instrument  200  is installed in such a way as to be able to suitably capture a region of interest including an eye of the target person P located in a capture volume. 
     The control unit  206  is connected to the imaging instrument  200 , and performs control of an imaging operation of the imaging instrument  200 . Further, the control unit  206  acquires the first image I 1  from the imaging instrument  200 , and supplies the first image I 1  to the image processing device  12 . 
     The image processing device  12  is a computer that extracts a feature value of an eye, particularly, an iris of the target person P from the first image I 1 , or the like. The image processing device  12  registers the extracted feature value in the database  30 . 
     Here, as illustrated in  FIG.  3   , at least a part of a body including an eye of the target person P is captured in the first image I 1  generated by the imaging instrument  200 . For example, when imaging is performed under a condition such as a long distance between the target person P and the imaging instrument  200 , a wide capturing visual field of the imaging instrument  200 , or the target person P who is moving, the first image I 1  may include an image region that represents a portion other than an eye in addition to or instead of the eye. As one example, an image region that represents hair has a pixel value similar to that of an iris and a pupil of an eye. Further, in a pixel region that represents a hole of a nose, a pixel of a pixel value similar to that of an iris and a pupil forms a substantially circular shape similar to the iris and the pupil. Therefore, when the image processing device  12  detects an eye region from the first image I 1 , the image that represents the portions may also be estimated to represent an eye and may be detected by mistake. 
     In the present second example embodiment, when a candidate region being an image region estimated to represent an eye of the target person P is detected from the first image I 1 , the image processing device  12  identifies an eye region being an image region that represents the eye of the target person P from the candidate region. Hereinafter, a case where the image processing device  12  detects a plurality of candidate regions (C 1  to C 5  in  FIG.  3   ) from the first image I 1  will be described, but the same also applies to a case where the image processing device  12  detects one candidate region from the first image I 1 . 
     Note that the database  30  is a storage medium that is connected to the image processing device  12  and stores a feature value of an eye, particularly, an iris of the target person P being used for biometric authentication. 
       FIG.  4    is a block diagram illustrating the configuration of the imaging system  2  according to the second example embodiment. As described above, the imaging system  2  includes the image processing device  12 , the imaging device  20 , and the database  30 . The description of the imaging device  20  and the database  30  will be omitted. 
     The image processing device  12  includes a first image acquisition unit  120 , a detection unit  122 , an identification unit  124 , a feature extraction unit  127 , and a registration processing unit  128 . 
     The first image acquisition unit  120  acquires the first image I 1  from the control unit  206  of the imaging device  20 . The first image I 1  is a captured image in which at least a part of a body of the target person P is captured. The first image acquisition unit  120  supplies the acquired first image I 1  to the detection unit  122 . 
     The detection unit  122  calculates a first evaluation value indicating a likelihood of an eye for each first evaluation target region being an image region of an evaluation target. The first evaluation target region is an image region being set based on the first image I 1 . Here, in the present second example embodiment, the first evaluation target region is an image region being set in the first image I 1 . However, instead of this, the first evaluation target region may be an image region being set in an image generated by converting the first image I 1  in such a way as to have a predetermined pixel number. 
     Here, the detection unit  122  calculates, for each first evaluation target region, a first evaluation value, based on a distribution of a pixel value of a pixel included in the region. The pixel value includes a brightness value, luminosity, or the like. Then, the detection unit  122  detects a plurality of candidate regions from the first image I 1 , based on the first evaluation value. The detection unit  122  supplies information about the plurality of detected candidate regions to the identification unit  124 . 
     The identification unit  124  sets each of the plurality of detected candidate regions as a new evaluation target, and calculates a second evaluation value indicating a likelihood of an eye for each second evaluation target region being an image region of the evaluation target. The second evaluation target region is an image region being set based on the detected candidate region. The second evaluation target region may be each candidate region, or may be an extraction region being an image region according to each candidate region included in the first image I 1 . Further, the second evaluation target region may be an image region being set in an image generated by converting each candidate region or each extraction region in such a way as to have a predetermined pixel number. 
     Here, the identification unit  124  calculates, for each second evaluation target region, a second evaluation value, based on a distribution of a pixel value of a pixel included in the region. Therefore, a difference is that the first evaluation value is an evaluation value being calculated for an image region set based on the first image I 1 , whereas the second evaluation value is an evaluation value being calculated for an image region set based on a detected candidate region. 
     Then, the identification unit  124  identifies an eye region from one or a plurality of candidate regions, based on such a second evaluation value. The identification unit  124  supplies information about the identified eye region to the feature extraction unit  127 . 
     The feature extraction unit  127  extracts a feature value of the identified eye region by a predetermined method. 
     The registration processing unit  128  is connected to the database  30 , and performs processing of registering information about the feature value of the eye region in the database  30 . 
       FIG.  5    is a flowchart illustrating processing of the imaging system  2  according to the second example embodiment. 
     First, in step S 20 , the control unit  206  of the imaging device  20  of the imaging system  2  controls the imaging instrument  200 , and causes the imaging instrument  200  to perform an operation of capturing at least a part of a body including an eye of the target person P. The imaging instrument  200  generates the first image I 1  being a captured image, and supplies the first image I 1  to the control unit  206 . Then, the control unit  206  transmits the first image I 1  to the image processing device  12 . 
     Next, in step S 22 , the image processing device  12  performs registration processing of a feature value described below, based on the first image I 1 , and registers the feature value in the database  30 . 
     Next, in step S 24 , the control unit  206  of the imaging device  20  determines whether there is a next target person P or whether to perform re-registration. When the control unit  206  determines that there is a next target person P or re-registration is performed (step S 24 : YES), the control unit  206  returns the processing to step S 20 , and, when the control unit  206  does not determine that described above (NO in step S 24 ), the control unit  206  ends the processing. 
     Next, the registration processing of the image processing device  12  in step S 22  illustrated in  FIG.  5    will be described by using  FIG.  6   .  FIG.  6    is a flowchart illustrating the registration processing of the image processing device  12  according to the second example embodiment. 
     First, in step S 30 , the first image acquisition unit  120  of the image processing device  12  acquires the first image I 1  from the control unit  206  of the imaging device  20 . The first image acquisition unit  120  supplies the first image I 1  to the detection unit  122 . 
     Next, in step S 31 , the detection unit  122  sets a plurality of first evaluation target regions, based on the first image I 1 , and calculates a first evaluation value for each of the first evaluation target regions. Calculation processing of a first evaluation value may be performed by using any technique in object detection. 
     One example of the technique is indicated below. 
     (First Evaluation Value Calculation Technique  1 ) 
     For example, the detection unit  122  performs binary processing and the like on the first image I 1 , and classifies pixels included in the first image I 1  into pixels having a pixel value equal to or more than a predetermined threshold value and the other pixels. Then, the detection unit  122  performs, for each of the first evaluation target regions, thinning processing, Hough transform, and the like on the pixels having the pixel value equal to or more than the predetermined threshold value, and calculates a degree of coincidence between a shape formed by the pixels having the pixel value equal to or more than the predetermined threshold value and a predetermined substantially circular shape. The predetermined substantially circular shape may be determined according to a shape of an iris and a pupil, particularly, a pupil of a person. 
     (First Evaluation Value Calculation Technique  2 ) 
     For example, the detection unit  122  calculates, for each of the first evaluation target regions, a degree of coincidence with a template having a pixel value distribution of a predetermined substantially circular shape. 
     (First Evaluation Value Calculation Technique  3 ) 
     For example, the detection unit  122  scans the first image I 1  by a search window in a predetermined size, and calculates a first evaluation value, based on a combination of a light and darkness difference in the search window, a combination of a brightness distribution, a combination of a distribution in a gradient direction of brightness, or the like. In this case, an image region overlapping the search window on the first image I 1  is a first evaluation target region. Then, the first evaluation value may be based on a Haar-Like feature value, a Local Binary Pattern (LBP) feature value, or a Histogram of Oriented Gradients (HOG) feature value in such a first evaluation target region. 
     (First Evaluation Value Calculation Technique  4 ) 
     For example, the detection unit  122  predicts a feature value for a first evaluation target region by using a learned convolutional neural network (CNN) with the first image I 1  or the above-described conversion image of the first image I 1  as an input. Here, the CNN may include a single shot multibox detector (SSD). In this case, the first evaluation target region corresponds to a default box being set in an input image. 
     Next, in step S 32 , the detection unit  122  determines whether there is a first evaluation target region having a first evaluation value equal to or more than a predetermined threshold value (first threshold value). When the detection unit  122  determines that there is the first evaluation target region (step S 32 : YES), the detection unit  122  proceeds the processing to step S 33 , and, when the detection unit  122  does not determine that described above (step S 32 : NO), the detection unit  122  ends the processing. 
     In step S 33 , the detection unit  122  determines that the first evaluation target region having the first evaluation value equal to or more than the first threshold value is a candidate region. In this way, the detection unit  122  detects a plurality of candidate regions. Then, the detection unit  122  supplies information about the plurality of candidate regions to the identification unit  124 . 
     Next, in step S 34 , the identification unit  124  sets, for each of the candidate regions, an image region based on the candidate region as a second evaluation target region, and calculates a second evaluation value for the second evaluation target region. Here, the image region based on the candidate region may be a candidate region, an extraction region, or an image region generated by converting a candidate region or an extraction region in such a way as to have a predetermined pixel number. 
     For example, in the second example embodiment, the identification unit  124  may calculate a second evaluation value by using a technique that is not adopted for the first evaluation value calculation processing by the detection unit  122  in step S 31  among the first evaluation value calculation techniques  1  to  4  indicated in step S 31  described above. However, at this time, the “detection unit  122 ” is replaced with the “identification unit  124 ”, the “first image I 1 ” and the “first evaluation target region” are replaced with the “second evaluation target region”, and the “first evaluation value” is replaced with the “second evaluation value”. 
     Further, for each second evaluation target region, the identification unit  124  may calculate, as a second evaluation value, a degree of a focus of an image region associated with an iris and a pupil, particularly, a pupil portion of a person being detected by the detection unit  122  in steps S 31  to S 33 . Specifically, for each second evaluation target region, the identification unit  124  may calculate a frequency spectrum distribution by using a fast Fourier transform for a brightness level, and may calculate a second evaluation value, based on spectrum strength in a predetermined frequency region. Further, the identification unit  124  may calculate a second evaluation value, based on a value acquired by performing Laplacian differentiation on a brightness value, for each second evaluation target region. 
     Next, in step S 35 , the identification unit  124  determines whether there is a second evaluation target region having a second evaluation value equal to or more than a predetermined threshold value (second threshold value). When the identification unit  124  determines that there is the second evaluation target region (step S 35 : YES), the identification unit  124  proceeds the processing to step S 36 , and, when the identification unit  124  does not determine that described above (step S 35 : NO), the identification unit  124  ends the processing. 
     Next, in step S 36 , the identification unit  124  identifies, as an eye region, a candidate region associated with the second evaluation target region having the second evaluation value equal to or more than the second threshold value. Then, the identification unit  124  supplies information about the identified eye region to the feature extraction unit  127 . 
     Next, in step S 37 , the feature extraction unit  127  extracts a feature value of the eye region. For example, the feature extraction unit  127  calculates a feature value of the identified eye region by using a learned CNN that outputs a feature value vector used for biometric authentication from an input image. Note that the input image may be an image of the identified eye region. The feature extraction unit  127  supplies information about the calculated feature value of the eye region to the registration processing unit  128 . 
     Then, in step S 38 , the registration processing unit  128  registers the information about the feature value of the eye region in the database  30 . 
     Note that the second evaluation value may be an index that evaluates a degree of suitability of a detection result of the detection unit  122  as an eye for extracting iris authentication information. In step S 37 , when the feature extraction unit  127  extracts a feature value related to iris authentication information, it is desired that the center of an image region associated with a pupil portion is disposed in a position as close to the center of an input image as possible. At this time, in step S 34 , for each second evaluation target region, the identification unit  124  may calculate, as a second evaluation value, a distance between the center of the image region associated with an iris and a pupil, particularly, a pupil portion being detected by the detection unit  122  in steps S 31  to S 33  and the center of the second evaluation target region. At this time, the identification unit  124  may use a learned CNN that predicts the distance. Since the CNN can be achieved by a light configuration, the image processing device  12  can perform highly accurate feature value extraction while reducing a calculation cost. 
     Further, the image processing device  12  may perform not only registration processing of a feature value but also update processing of a feature value. In this case, instead of step S 38 , the registration processing unit  128  updates the information about the feature value of the eye region as a record of the target person P included in the database  30 . Further, the image processing device  12  may perform authentication processing, based on an extracted feature value. In this case, instead of step S 38 , the registration processing unit  128  performs the authentication processing by verifying the extracted feature value with a feature value being stored in the database  30 , and determining whether there is a coinciding feature value. 
     In this way, according to the second example embodiment, the image processing device  12  identifies an eye region, based on different evaluation values at two stages indicating a likelihood of an eye. Therefore, in a case where imaging is performed under a condition of a wide capturing visual field of a camera and the like, even when an image region that represents a portion other than an eye is included in a first image being captured, extraction of a false feature value and registration can be avoided. In this way, authentication performance when the authentication processing is performed by using a registered feature value can be improved. 
     Further, the image processing device  12  according to the second example embodiment automatically performs calculation of evaluation values at two stages. Thus, whether a detected region is an eye does not need to be checked by an operator, and a load on the operator can be reduced. Further, in this way, real time property of the registration processing can be ensured. 
     THIRD EXAMPLE EMBODIMENT 
     Next, a third example embodiment according to this disclosure will be described by using  FIGS.  7  to  8   . The third example embodiment has a characteristic that a candidate region or a second evaluation target region is displayed on a display unit. An imaging system  2  according to the third example embodiment has a configuration and a function similar to those of the imaging system  2  according to the second example embodiment, and an image processing device  12  according to the third example embodiment is a computer having a configuration and a function similar to those of the image processing device  12  according to the second example embodiment, or the like. However, the image processing device  12  according to the third example embodiment is different from the second example embodiment in a point that the image processing device  12  according to the third example embodiment further includes a display unit that displays a candidate region or a second evaluation target region in registration processing. 
       FIGS.  7  and  8    are diagrams for describing one example of display in the registration processing of the image processing device  12  according to the third example embodiment. 
     For example, when it is determined that there is a second evaluation target region having a second evaluation value equal to or more than a second threshold value in step S 35  illustrated in  FIG.  6   , an identification unit  124  causes the display unit to display the second evaluation target region or a candidate region associated with the second evaluation target region as illustrated in  FIG.  7   , instead of step S 36 . At this time, the identification unit  124  may prompt an input of whether to register a region displayed for an operator as a feature value of an eye region. The identification unit  124  identifies, as the eye region, a candidate region being the displayed region in response to reception of the input for registration. 
     Further, instead of step S 35  illustrated in  FIG.  6   , the identification unit  124  causes the display unit to display each candidate region or each second evaluation target region as illustrated in  FIG.  8    while providing a number associated with an order of magnitude of an associated second evaluation value. The identification unit  124  may cause the display unit to display a second evaluation value associated with each region instead of or in addition to the number. Then, the identification unit  124  may prompt a selection of an eye region whose feature value is to be registered among regions displayed for an operator. The identification unit  124  identifies, as the eye region, a candidate region being the selected region in response to reception of the selection. 
     FOURTH EXAMPLE EMBODIMENT 
     Next, a fourth example embodiment according to this disclosure will be described by using  FIGS.  9  to  11   . The fourth example embodiment has a characteristic that a second image generated based on an extraction region according to a candidate region is used for calculation of a second evaluation value. 
       FIG.  9    is a block diagram illustrating a configuration of an imaging system  3  according to the fourth example embodiment. The imaging system  3  according to the fourth example embodiment basically has a configuration and a function to those of the imaging system  2  according to the second to third example embodiments. However, the imaging system  3  includes an image processing device  13  instead of the image processing device  12 . 
     The image processing device  13  is a computer basically having a configuration and a function similar to those of the image processing device  12 , or the like. However, the image processing device  13  includes an identification unit  134  instead of the identification unit  124 . 
     The identification unit  134  basically has a function similar to that of the identification unit  124 , but includes an extraction image generation unit  135  and an evaluation unit  136 . 
     The extraction image generation unit  135  cuts an extraction region according to a candidate region from a first image I 1 , and generates a second image I 2 , based on the cut extraction region. In other words, in the present fourth example embodiment, the second image I 2  is a second evaluation target region. The second image I 2  has a predetermined pixel number according to the following eye evaluation model. The extraction image generation unit  135  supplies the second image I 2  to the evaluation unit  136 . 
     The evaluation unit  136  calculates a second evaluation value by using a learned eye evaluation model with the second image I 2  as input data. In the present fourth example embodiment, the learned eye evaluation model includes a learned CNN that outputs a second evaluation value from the second image I 2 . The evaluation unit  136  identifies an eye region from one or a plurality of candidate regions, based on the calculated second evaluation value. Then, the evaluation unit  136  supplies information about the identified eye region to a feature extraction unit  127 . 
     Next, by using  FIG.  10   , second evaluation value calculation processing of the identification unit  134  associated with step S 34  illustrated in  FIG.  6    will be described with reference to  FIG.  11   .  FIG.  10    is a flowchart illustrating the second evaluation value calculation processing of the image processing device  13  according to the fourth example embodiment.  FIG.  11    is a diagram for describing the second evaluation value calculation processing of the image processing device  13  according to the fourth example embodiment. Note that X 1 , XC1, XE1, XC2, Y 1 , YC1, YE1, and YC2 described below are all natural numbers. 
     First, in step S 40 , the extraction image generation unit  135  of the identification unit  134  of the image processing device  13  identifies an extraction region from the first image I 1 , and cuts the extraction region. Here, as illustrated in  FIG.  11   , for example, the first image I 1  has a pixel number in a width direction of X 1  and a pixel number in a height direction of Y 1 . Further, a candidate region C 1  being one of a plurality of candidate regions has a pixel number in the width direction of XC1 and a pixel number in the height direction of YC1. At this time, the extraction image generation unit  135  identifies, as an extraction region E 1  according to the candidate region C 1 , an image region having a pixel number in the width direction of XE1 (≥XC1) and a pixel number in the height direction of YE1 (≥YC1) around a central point of the candidate region C 1 . The extraction image generation unit  135  identifies an extraction region similarly for another candidate region. 
     Next, in step S 42 , the extraction image generation unit  135  cuts, for each candidate region, the identified extraction region from the first image I 1 , and generates the second image I 2  having a predetermined pixel number by using the cut extraction region. As illustrated in  FIG.  11   , the second image I 2  generated for the candidate region C 1  is an image having a pixel number in the width direction of XC2 and a pixel number in the height direction of YC2. XC2 and YC2 may be a predetermined value, for example, 300 according to a learned eye evaluation model. The extraction image generation unit  135  generates the second image I 2  by converting the extraction region E 1  in such a way that the pixel number is changed from XE1×YE1 to XC2×YC2. In other words, the extraction image generation unit  135  may generate the second image I 2  through conversion by enlarging, reducing, extending, or compressing the extraction region E 1 . For example, when the extraction region E 1  is enlarged or reduced, the extraction image generation unit  135  may change an interval between a predetermined pixel and a surrounding pixel that are included in the extraction region E 1 , and may interpolate a pixel therebetween. Further, when the extraction region E 1  is extended, the extraction image generation unit  135  may extend an interval between pixels for pixels in an extending direction, and may interpolate a pixel therebetween. Further when the extraction region E 1  is compressed, the extraction image generation unit  135  may reduce an interval between pixels for pixels in a compressing direction, and may appropriately interpolate a pixel. 
     Note that the extraction image generation unit  135  may appropriately perform normalization processing of a pixel value on the generated second image I 2 . The normalization processing may include binary processing. Then, the extraction image generation unit  135  supplies the second image I 2  to the evaluation unit  136 . 
     Next, in step S 44 , the evaluation unit  136  inputs the second image I 2  to an input layer of the CNN of the learned eye evaluation model as illustrated in  FIG.  11   . 
     Next, in step S 46 , the evaluation unit  136  acquires a second evaluation value S from an output layer of the CNN of the learned eye evaluation model as illustrated in  FIG.  11   . In this way, the evaluation unit  136  calculates the second evaluation value S associated with the candidate region. 
     Next, in step S 48 , the evaluation unit  136  determines whether calculation of the second evaluation value S for all candidate regions is completed. When the evaluation unit  136  determines that it is completed (step S 48 : YES), the evaluation unit  136  ends the processing, and, when the evaluation unit  136  does not determine that described above (step S 48 : NO), the evaluation unit  136  returns the processing to step S 40 . 
     In this way, according to the fourth example embodiment, the image processing device  13  calculates the second evaluation value S from the second image I 2  based on an extraction region acquired by eliminating an unnecessary region from the first image I 1 . Note that the second image I 2  is resized to a predetermined image size around the center of the candidate region C, that is, around a pupil. Therefore, a more precise eye evaluation can be performed while suppressing a calculation cost as compared to detection by a first evaluation value. In this way, a feature value having a higher degree of authentication performance can be registered while acquiring an effect similar to that in the second to third example embodiments. 
     FIFTH EXAMPLE EMBODIMENT 
     Next, a fifth example embodiment according to this disclosure will be described by using  FIGS.  12  to  17   . The fifth example embodiment has a characteristic that a second evaluation value is calculated based on an irradiation pattern of light applied to a target person P. 
       FIG.  12    is a schematic configuration diagram of an imaging system  4  according to the fifth example embodiment. The imaging system  4  according to the fifth example embodiment basically has a configuration and a function to those of the imaging system  3  according to the fourth example embodiment. However, the imaging system  4  according to the fifth example embodiment includes an imaging device  24  instead of the imaging device  20  and an image processing device  14  instead of the image processing device  13 . 
     The imaging device  24  basically has a configuration and a function similar to those of the imaging device  20 , but includes a light source array  244  and a control unit  246  instead of the control unit  206 . 
     The light source array  244  applies light having a predetermined irradiation pattern to the target person P. The light source array  244  may apply near infrared light. The light source array  244  applies light in such a way that a reflection pattern is formed on an eye of the target person P. Note that it is desired that the light source array  244  applies light in such a way that a reflection pattern is formed on a pupil portion except for an iris portion of the eye of the target person P. The light source array  244  may be installed in a position determined based on an assumed standing position of the target person P and a position in which the imaging instrument  200  is installed in order to apply light to the eye, particularly, the pupil of the target person P in such a manner. In the present fifth example embodiment, the light source array  244  includes a plurality of light sources. At this time, an irradiation pattern may be determined by an arrangement of each of the light sources of the light source array  244  and an irradiation direction of each of the light sources. For example, the light source array  244  may include a right light source array and a left light source array being symmetrically disposed in a direction (that is, a left-right direction of the target person P) orthogonal to an optical axis direction of an imaging instrument  200  and a height direction around the optical axis direction. Each of the right light source array and the left light source array may include a plurality of light sources disposed in such a way that light is applied to the target person P at a predetermined interval in the height direction. 
     The control unit  246  controls an operation of the light source array  244  in addition to the function of the control unit  206 . In response to application of light from the light source array  244 , the control unit  246  controls an imaging operation of the imaging instrument  200 , and acquires a first image I 1  from the imaging instrument  200 . Then, the control unit  246  supplies the first image I 1  to the image processing device  14 . 
       FIG.  13    is a block diagram illustrating a configuration of the imaging system  4  according to the fifth example embodiment. As described above, the imaging system  4  includes the imaging device  24 , the image processing device  14 , and a database  30 . Here, only a configuration of the image processing device  14  will be described. 
     The image processing device  14  is a computer basically having a configuration and a function similar to those of the image processing device  13 , or the like. However, the image processing device  14  includes an identification unit  144  instead of the identification unit  134 . 
     The identification unit  144  includes an extraction image generation unit  145  and an evaluation unit  146 . 
     The extraction image generation unit  145  basically has a function similar to that of the extraction image generation unit  135 , cuts an extraction region according to a candidate region from the first image I 1 , and generates a second image I 2 , based on the cut extraction region. 
     The evaluation unit  146  calculates a second evaluation value, based on a reflection pattern formed in a candidate region in response to application of light of the light source array  244  to the target person P. 
     Next, by using  FIG.  14   , second evaluation value calculation processing of the identification unit  144  associated with step S 34  illustrated in  FIG.  6    will be described with reference to  FIG.  15   .  FIG.  14    is a flowchart illustrating the second evaluation value calculation processing of the image processing device  14  according to the fifth example embodiment. Note that a step similar to the step illustrated in  FIG.  10    is provided with the same symbol, and description will be appropriately omitted. Further,  FIG.  15    is a diagram for describing the second evaluation value calculation processing of the image processing device  14  according to the fifth example embodiment. 
     In step S 60 , similarly to step S 40  illustrated in  FIG.  10   , the extraction image generation unit  145  of the identification unit  144  of the image processing device  14  identifies an extraction region from the first image I 1 , and cuts the extraction region. Here, as illustrated in  FIG.  15   , the extraction image generation unit  145  identifies, as an extraction region E 1  according to a candidate region C 1 , an image region having a pixel number of XE1×YE1 around a central point of the candidate region C 1  having a pixel number of XC1×YC1. XE1 and YE1 may be predetermined according to a pixel number of a template described below. 
     Next, in step S 62 , the extraction image generation unit  145  cuts, for each candidate region, the identified extraction region E 1  from the first image I 1 , and generates the second image I 2  by using the cut extraction region E 1 . As illustrated in  FIG.  15   , the second image I 2  may have a pixel number of XE1×YE1 similarly to the extraction region E 1 . In other words, the extraction image generation unit  145  may replace the cut extraction region E 1  with the second image I 2  without converting a pixel of the extraction region E 1 . However, the present example embodiment is not limited to this, and the extraction image generation unit  145  may generate the second image I 2  by converting a pixel in such a way that the extraction region E 1  has a pixel number of XC2×YC2 similarly to step S 42  illustrated in  FIG.  10   . Note that XC2 and YC2 may be predetermined according to a pixel number of a template described below. Then, the extraction image generation unit  145  supplies the second image I 2  to the evaluation unit  146 . 
     Next, in step S 64 , the evaluation unit  146  acquires a template generated based on a reflection pattern of a cornea being associated with an irradiation pattern of the light source array  244 . The template may have a pixel value distribution according to the reflection pattern of the cornea being associated with the irradiation pattern of the light source array  244 . 
     Next, in step S 66 , the evaluation unit  146  matches the template with the second image I 2 , and calculates a degree of coincidence between the second image I 2  and the template. As illustrated in  FIG.  15   , when a candidate region is an eye region, a reflection pattern R similar to the reflection pattern of the cornea is projected in a region associated with a pupil of the second image I 2 . In  FIG.  15   , an irradiation pattern has a symmetrical shape, and the reflection pattern R has a substantially symmetrical shape according to the irradiation pattern by cornea reflection. As one example, the reflection pattern R includes a reflection pattern on a left side and a reflection pattern on a right side, and both of the reflection patterns include spots arranged at a substantially regular interval therebetween in the height direction. Note that, when a candidate region is other than an eye region, irradiated light is scattered, and thus such a reflection pattern R cannot be acquired. Therefore, when a candidate region is an eye region, a degree of coincidence with the template is relatively increased. 
     Next, in step S 68 , the evaluation unit  146  sets, as a second evaluation value, the degree of coincidence associated with the candidate region. In this way, the evaluation unit  146  calculates the second evaluation value associated with the candidate region. 
     Note that the processing illustrated in step S 60  may be omitted. In this case, in step S 62 , the “extraction region” is replaced with the “candidate region”, “XE1” is replaced with “XC1”, and “YE1” is replaced with “YC1”. 
     Note that, in the fifth example embodiment, the evaluation unit  146  calculates a second evaluation value for a candidate region by using template matching. However, the present example embodiment is not limited to this, and the evaluation unit  146  may calculate a second evaluation value by using a learned reflection pattern evaluation model with the second image I 2  as input data. The learned reflection pattern evaluation model includes a learned CNN that outputs a second evaluation value from the second image I 2  in which the reflection pattern R is projected. 
     Note that, in order to further facilitate detection of the reflection pattern R, an irradiation pattern of light applied from the light source array  244  preferably has an asymmetrical shape.  FIGS.  16  to  17    are diagrams for describing the reflection pattern R with respect to an irradiation pattern of light of the light source array  244  according to the fifth example embodiment.  FIGS.  16  and  17    illustrate the second image I 2  in which the reflection pattern R is projected and that is associated with the candidate region C 1 . 
     As illustrated in  FIGS.  16  and  17   , when a candidate region is an eye region, the reflection pattern R has an asymmetrical shape according to an asymmetrical shape of an irradiation pattern by cornea reflection. Note that, when a candidate region is other than an eye region, irradiated light is scattered, and thus such a reflection pattern R cannot be acquired. 
       FIG.  16    illustrates the second image I 2  when, as one example, a reflection pattern on a left side includes spots arranged at an irregular interval therebetween in the height direction. Therefore, the reflection pattern on the left side and a reflection pattern on a right side are asymmetrical with respect to a central axis of the second image I 2  indicated by a broken line. At this time, the light source array  244  may include a right light source array and a left light source array, and the left light source array may include a plurality of light sources disposed in such a way that light is applied to the target person P at an irregular interval in the height direction. 
       FIG.  17    illustrates the second image I 2  when, as one example, the reflection pattern R includes spots in which a spot shape is an asymmetrical shape. An irradiation pattern in this case may be formed by combining light having different spot diameters of a plurality of light sources. 
     Further, an irradiation pattern in this case may be formed by forming a spot shape to be asymmetrical by a filter or the like for light of a single light source. At this time, the light source array  244  may be a single light source instead of including a plurality of light sources. 
     In this way, according to the fifth example embodiment, the light source array  244  of the imaging system  4  applies light and forms a reflection pattern associated with a predetermined irradiation pattern on a body portion of the target person P associated with a candidate region. Then, the image processing device  14  calculates a second evaluation value, based on the reflection pattern in the candidate region. Therefore, whether the candidate region is an eye region can be more precisely evaluated. In this way, a feature value having a higher degree of authentication performance can be registered while acquiring an effect similar to that in the second to third example embodiments. 
     SIXTH EXAMPLE EMBODIMENT 
     Next, a sixth example embodiment according to this disclosure will be described by using  FIGS.  18  to  20   . The sixth example embodiment has a characteristic that an imaging instrument and a light source array are controlled based on a whole captured image of a target person P. 
       FIG.  18    is a schematic configuration diagram of an imaging system  6  according to the sixth example embodiment. Further,  FIG.  19    is a block diagram illustrating a configuration of the imaging system  6  according to the sixth example embodiment. The imaging system  6  according to the sixth example embodiment basically has a configuration and a function to those of the imaging system  4  according to the fifth example embodiment. However, the imaging system  6  according to the sixth example embodiment includes an imaging device  26  instead of the imaging device  24 . 
     The imaging device  26  basically has a configuration and a function similar to those of the imaging device  24 , but includes a plurality of individual imaging instruments  260 , a whole imaging instrument  262 , and a control unit  266  instead of the imaging instrument  200  and the control unit  246 . 
     The plurality of individual imaging instruments  260  are each a camera having a function similar to that of the imaging instrument  200 , and are disposed in positions different from each other in the same visual field range. 
     The whole imaging instrument  262  is a camera for capturing a whole of the target person P. The whole imaging instrument  262  captures the target person P in a visual field range wider than the visual field range of the individual imaging instrument  260  in such a way as to be able to cover a range of a great height to a small height of the target person P. Note that, it is assumed that the whole imaging instrument  262  has a resolution to a degree that a position of a face, particularly, around an eye of the target person P can be estimated. Then, the whole imaging instrument  262  generates a whole captured image of the target person P. 
     The control unit  266  basically has a function similar to that of the control unit  246 , but controls an operation of a light source array  244  in such a way that the light source array  244  suitably applies light to an eye, based on an image of the whole imaging instrument  262 . Further, the control unit  266  selects the individual imaging instrument  260  that can suitably capture an eye, based on a whole captured image supplied from the whole imaging instrument  262 , and controls an imaging operation of the selected individual imaging instrument  260 . 
       FIG.  20    is a flowchart illustrating processing of the imaging system  6  according to the sixth example embodiment. A step similar to the step illustrated in  FIG.  5    is provided with the same symbol, and description will be omitted. 
     First, in step S 70 , the control unit  266  of the imaging device  26  of the imaging system  6  controls the whole imaging instrument  262 , and causes the whole imaging instrument  262  to perform an operation of capturing a whole of the target person P. The whole imaging instrument  262  generates a hole captured image, and supplies the whole captured image to the control unit  266 . 
     Next, in step S 72 , the control unit  266  estimates a position around an eye of the target person P in the whole captured image being supplied from the whole imaging instrument  262 . Then, the control unit  266  selects the individual imaging instrument  260  associated with the position around the eye of the target person P in the whole captured image by using a camera parameter and an arrangement relationship of the whole imaging instrument  262  and each of the individual imaging instruments  260 . 
     In step S 74 , the control unit  266  adjusts at least one of an arrangement and an orientation of an optical axis of the light source array  244  according to the derived position around the eye and an arrangement relationship between the target person P and the whole imaging instrument  262 . Then, the control unit  266  causes the light source array  244  to apply light to the target person P. 
     Next, in step S 76 , the control unit  266  controls the selected individual imaging instrument  260 , and causes the individual imaging instrument  260  to perform an operation of capturing at least a part of a body including the eye of the target person P. The individual imaging instrument  260  generates a first image I 1  being a captured image, and supplies the first image I 1  to the control unit  266 . Then, the control unit  266  transmits the first image I 1  to an image processing device  14 . 
     In this way, according to the sixth example embodiment, the imaging system  6  estimates a position around an eye from a whole captured image, adjusts a position of the light source array  244 , based on the estimated position, and selects the individual imaging instrument  260  that suitably captures the eye. In this way, a feature value having a higher degree of authentication performance can be registered while acquiring an effect similar to that in the second to fifth example embodiments. 
     A computer in the first to sixth example embodiments described above is formed of a computer system including a personal computer, a word processor, and the like. However, this disclosure is not limited to this, and the computer can also be formed of a computer system connected onto a server of a local area network (LAN), a host of computer (personal computer) communication, or the Internet, and the like. Further, the computer can also be formed of the entire network by distributing a function to each apparatus on the network. 
     Note that, in the first to sixth example embodiments, this disclosure has been described above as a configuration of hardware, but this disclosure is not limited to this. This disclosure can also achieve various types of processing such as the imaging control processing, the registration processing, and the light source control processing described above by causing a processor  1010  described below to execute a computer program. 
       FIG.  21    is one example of a configuration diagram of a computer  1900  according to the first to sixth example embodiments. As illustrated in  FIG.  21   , the computer  1900  includes a control processing unit  1000  for controlling the entire system. An input device  1050 , a storage device  1200 , a storage medium driving device  1300 , a communication control device  1400 , and an input/output I/F  1500  are connected to the control processing unit  1000  via a bus line such as a data bus. 
     The control processing unit  1000  includes the processor  1010 , a ROM  1020 , and a RAM  1030 . 
     The processor  1010  performs various types of information processing and control according to a program stored in various storage units such as the ROM  1020  and the storage device  1200 . 
     The ROM  1020  is a read-only memory that previously stores various programs and data for the processor  1010  to perform various types of control and computations. 
     The RAM  1030  is a random-access memory used as a work memory by the processor  1010 . Various areas for performing various types of processing according to the present first to sixth example embodiments can be secured in the RAM  1030 . 
     The input device  1050  is an input device that receives an input from a user, such as a keyboard, a mouse, and a touch panel. For example, various keys such as numeric keys, a functional key for performing various functions, and a cursor key are disposed in a keyboard. A mouse is a pointing device, and is an input device that designates an associated function by clicking a key, an icon, and the like displayed on the display device  1100 . A touch panel is an input apparatus disposed on a surface of the display device  1100 , and specifies a touch position of a user being associated with various operation keys displayed on the screen of the display device  1100 , and receives an input of the operation key displayed in association with the touch position. 
     For example, a CRT, a liquid crystal display, or the like is used for the display device  1100 . The display device displays an input result by a keyboard or a mouse, and displays searched image information in the end. Further, the display device  1100  displays an image of an operation key for performing various necessary operations from a touch panel according to various functions of the computer  1900 . 
     The storage device  1200  is formed of a storage medium that can perform reading and writing, and a driving device for reading and writing various types of information, such as a program and data, from and to the storage medium. 
     As the storage medium used for the storage device  1200 , a hard disk or the like is mainly used, but a non-transitory computer-readable medium used in the storage medium driving device  1300  described below may be used. 
     The storage device  1200  includes a data storage unit  1210 , a program storage unit  1220 , another storage unit (for example, a storage unit for backing up a program, data, and the like being stored in the storage device  1200 ) that is not illustrated, and the like. The program storage unit  1220  stores a program for achieving various types of processing in the present first to sixth example embodiments. The data store unit  1210  stores various types of data of various databases according to the present first to sixth example embodiments. 
     The storage medium driving device  1300  is a driving device for the processor  1010  to read a computer program, data including a document, and the like from a storage medium (external storage medium) outside. 
     Herein, the external storage medium refers to a non-transitory computer-readable medium that stores a computer program, data, and the like. The non-transitory computer-readable medium includes a tangible storage medium of various types. Examples of the non-transitory computer-readable medium include a magnetic recording medium (for example, a flexible disk, a magnetic tape, and a hard disk drive), a magneto-optical recording medium (for example, a magneto-optical disk), a CD-read only memory (CD-ROM), a CD-R, a CD-R/W, and a semiconductor memory (for example, a mask ROM, a programmable ROM (PROM), an erasable PROM (EPROM), a flash ROM, and a random access memory (RAM)). Further, various programs may be supplied to a computer by a transitory computer-readable medium of various types. Examples of the transitory computer-readable medium include an electric signal, an optical signal, and an electromagnetic wave. The transitory computer-readable medium can supply various programs to the computer  1900  via a wired communication path such as an electric wire and an optical fiber or a wireless communication path and the storage medium driving device  1300 . 
     In other words, in the computer  1900 , various programs from the external storage medium being set for the storage medium driving device  1300  are read by the processor  1010  of the control processing unit  1000 , and are stored in each of the units of the storage device  1200 . 
     Then, when the computer  1900  performs various types of processing, a corresponding program is read from the storage device  1200  into the RAM  1030  and is executed. However, the computer  1900  can also read and execute a program directly into the RAM  1030  from the external storage medium, instead of the storage device  1200 , by the storage medium driving device  1300 . Further, depending on a computer, various programs and the like may be stored in advance in the ROM  1020  and may be executed by the processor  1010 . Furthermore, the computer  1900  may download various programs and data from another storage medium via the communication control device  1400  and may execute the various programs and the data. 
     The communication control device  1400  is a control device for network-connecting the computer  1900  and various external electronic apparatuses such as another personal computer and another word processor. The communication control device  1400  can access the computer  1900  from various external electronic apparatuses. 
     The input/output I/F  1500  is an interface for connecting various input/output devices via a parallel port, a serial port, a keyboard port, a mouse port, or the like. 
     Note that, as the processor  1010 , a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), a digital signal processor (DSP), an application specific integrated circuit (ASIC), and the like may be used. Further, a plurality thereof may be used in parallel. 
     An execution order of each processing in the system and the method indicated in the claims, the specification, and the drawings is not particularly stated such as “before” and “prior to”, and any order can be achieved as long as an output of previous processing is not used for subsequent processing. Even when “first”, “next”, or the like is used for the sake of convenience to describe an operation flow in the claims, the specification, and the drawings, it does not mean that execution in this order is essential. 
     Although this disclosure has been described with reference to the example embodiments, this disclosure is not limited to the above-described example embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and the details of this disclosure within the scope of the invention. A part or the whole of the above-described example embodiments may also be described in supplementary notes below, which is not limited thereto. 
     (Supplementary Note 1) 
     An image processing system comprising: 
     a detection unit configured to detect, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value; 
     an identification unit configured to identify, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value; and 
     a feature extraction unit configured to extract a feature value of the identified eye region, wherein 
     the first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image, and 
     the second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     (Supplementary Note 2) 
     The image processing system according to Supplementary Note 1, wherein the identification unit includes an evaluation unit configured to calculate the second evaluation value, based on a reflection pattern formed in the candidate region in response to application of light having a predetermined irradiation pattern to the target person. 
     (Supplementary Note 3) 
     The image processing system according to Supplementary Note 2, wherein the irradiation pattern has an asymmetrical shape. 
     (Supplementary Note 4) 
     The image processing system according to any one of Supplementary Notes 1 to 3, wherein 
     the identification unit includes
         an extraction image generation unit configured to cut an extraction region according to the candidate region from the first image, and generate a second image, based on the cut extraction region, and   an evaluation unit configured to calculate the second evaluation value by using a learned eye evaluation model with the second image as input data.       

     (Supplementary Note 5) 
     An imaging system comprising: 
     an imaging device configured to capture an image of a target person, and generate a first image; and 
     an image processing device including: a detection unit configured to detect, from the first image, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value; an identification unit configured to identify, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value; and a feature extraction unit configured to extract a feature value of the identified eye region, wherein 
     the first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image, and 
     the second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     (Supplementary Note 6) 
     The imaging system according to Supplementary Note 5, further comprising a light source array configured to apply light having a predetermined irradiation pattern to the target person. 
     (Supplementary Note 7) 
     The imaging system according to Supplementary Note 6, wherein the irradiation pattern has an asymmetrical shape. 
     (Supplementary Note 8) 
     The imaging system according to any one of Supplementary Notes 5 to 7, wherein 
     the imaging device further includes
         a plurality of individual imaging instruments disposed in positions different from one another in the same visual field range, and   a whole imaging instrument configured to perform imaging in a visual field range wider than a visual field range of the individual imaging instrument.       

     (Supplementary Note 9) 
     An image processing method comprising: 
     a detection step of detecting, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value; 
     an identification step of identifying, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value; and 
     a feature extraction step of extracting a feature value of the identified eye region, wherein 
     the first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image, and 
     the second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     (Supplementary Note 10) 
     A non-transitory computer-readable medium configured to store an image processing program causing a compute to execute an image processing method including: 
     a detection step of detecting, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value; 
     an identification step of identifying, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value; and 
     a feature extraction step of extracting a feature value of the identified eye region, wherein 
     the first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image, and 
     the second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region. 
     REFERENCE SIGNS LIST 
     
         
           2 ,  3 ,  4 ,  6  IMAGING SYSTEM 
           10  IMAGE PROCESSING SYSTEM 
           12 ,  13 ,  14  IMAGE PROCESSING DEVICE 
           20 ,  24 ,  26  IMAGING DEVICE 
           30  DATABASE 
           102 ,  122  DETECTION UNIT 
           120  FIRST IMAGE ACQUISITION UNIT 
           104 ,  124 ,  134 ,  144  IDENTIFICATION UNIT 
           107 ,  127  FEATURE EXTRACTION UNIT 
           128  REGISTRATION PROCESSING UNIT 
           135 ,  145  EXTRACTION IMAGE GENERATION UNIT 
           136 ,  146  EVALUATION UNIT 
           200  IMAGING INSTRUMENT 
           206 ,  246 ,  266  CONTROL UNIT 
           244  LIGHT SOURCE ARRAY 
           260  INDIVIDUAL IMAGING INSTRUMENT 
           262  WHOLE IMAGING INSTRUMENT 
         P TARGET PERSON 
         I 1  FIRST IMAGE 
         I 2  SECOND IMAGE 
         C 1  TO C 5  CANDIDATE REGION 
         E 1  EXTRACTION REGION 
         S SECOND EVALUATION VALUE 
           1000  CONTROL PROCESSING UNIT 
           1010  PROCESSOR 
           1020  ROM 
           1030  RAM 
           1050  INPUT DEVICE 
           1100  DISPLAY DEVICE 
           1200  STORAGE DEVICE 
           1210  DATA STORAGE UNIT 
           1220  PROGRAM STORAGE UNIT 
           1300  STORAGE MEDIUM DRIVING DEVICE 
           1400  COMMUNICATION CONTROL DEVICE 
           1500  INPUT/OUTPUT I/F 
           1900  COMPUTER