JP6289308B2 - Information processing apparatus and program - Google Patents

Description

  Embodiments described herein relate generally to an information processing apparatus and a program.

  2. Description of the Related Art Conventionally, a person authentication system that authenticates a person's face included in an image captured by a camera has been proposed. The person authentication system determines that the same person is the same person when the detected face images of the same person are continuously reflected over a plurality of frames in a series of continuous imaging data.

  However, in the conventional person authentication system, when a face image of a detected person is out of frame from a series of continuous imaging data, the already detected person is determined to be another person and new identification information is assigned. End up. For this reason, the conventional person authentication system cannot determine that the person who is out of the frame is the same person even if the person enters the frame again. Thus, the conventional person authentication system cannot continuously track a specific person. For the same reason, the conventional person authentication system cannot track a person using a plurality of cameras.

  The problem to be solved by the present invention is to provide an information processing apparatus and program capable of determining that a person who has been framed in again after being out of frame is the same person.

The information processing apparatus according to the embodiment includes an input unit, a detection unit, an authentication unit, a registration unit, and an exclusion unit . The input unit receives input of imaging data captured by the imaging unit. The detection means detects a person from the imaging data received by the input means. The authentication unit authenticates whether the person detected by the detection unit is a person registered in a dictionary in which a person included in imaging data captured by the imaging unit in the past is registered. When the authentication unit authenticates that the person is registered in advance, the registration unit additionally registers imaging data captured by the imaging unit to the person in the dictionary. The excluding unit is configured to identify a person included in the image data on the authentication unit on the condition that the imaging data received by the input unit is image data captured by an imaging unit that captures a person who leaves. Exclude from the target.

FIG. 1 is an explanatory diagram schematically showing the configuration of the person authentication system. FIG. 2 is an explanatory diagram showing an example when the person authentication system is installed in a store. FIG. 3 is a block diagram illustrating a hardware configuration of the server apparatus. FIG. 4 is an explanatory diagram showing an example of the data configuration of the person authentication dictionary. FIG. 5 is a block diagram illustrating a hardware configuration of the camera. FIG. 6 is a block diagram showing a functional configuration of the person authentication system. FIG. 7 is a flowchart showing the flow of the person authentication process. FIG. 8 is a flowchart showing the flow of the exclusion process.

  Hereinafter, an information processing apparatus and a program according to embodiments will be described in detail with reference to the accompanying drawings. In addition, this invention is not limited by this embodiment.

  FIG. 1 is an explanatory diagram schematically showing the configuration of the person authentication system 1. As shown in FIG. 1, the person authentication system 1 includes a server device 10 and two types of cameras 20a and 20b having different uses. The camera 20a performs face authentication as to whether or not the person is registered in a person authentication dictionary D (see FIG. 3) described later, and captures a moving image for registration in the person authentication dictionary D (see FIG. 3). Used in. On the other hand, the camera 20b is used for capturing a moving image for excluding a person registered in the person authentication dictionary D (see FIG. 3) from the person authentication target. The person authentication system 1 includes one or more cameras 20a and 20b, respectively, and images a predetermined area. Here, the server apparatus 10 and the cameras 20 a and 20 b are connected by a LAN (Local Area Network) 30. The LAN 30 may be wired or wireless. The LAN 30 may be various public networks.

  The server apparatus 10 is an information processing apparatus that authenticates a person from a moving image captured by the cameras 20a and 20b and extracts person authentication information from the moving image. Then, the server device 10 stores the extracted person authentication information for each person. At that time, the server device 10 determines whether or not the person authentication information of the person is stored. When the person authentication information of the person is stored, the server device 10 stores the person authentication information in association with the person identification information of the person. On the other hand, when the person authentication information of the person is not stored, the server device 10 newly stores the person authentication information of the person. The server device 10 may be a plurality of clustered information processing devices.

  Next, a case where the person authentication system 1 is installed in a store M such as a supermarket will be described as an example. Here, FIG. 2 is an explanatory diagram showing an example when the person authentication system 1 is installed in the store M.

  The store M shown in FIG. 2 roughly includes a backyard M1, a sales floor M2, and an entrance M3. The backyard M1 is a place where operations relating to store operations such as a product warehouse, product packaging, and product ordering are performed. The server device 10 is installed in the backyard M1.

  The sales floor M2 is a place where products are sold. In addition, the sales floor M2 includes a camera 20a that captures a person traveling around the store M, a POS (Point Of Sale) terminal 40 for registering sales of products that the person purchases, and a shelf 50 that displays the products. Provided. When the person visits the store M, the person goes around the sales floor M2 and selects a product to be purchased. At that time, the camera 20a images a person. And the server apparatus 10 authenticates the person included in the moving image imaged by the camera 20a, and records the identification information of the camera 20a imaged for each person and the imaging time. Thereby, action can be tracked for every person. In the store M shown in FIG. 2, five cameras 20a, three POS terminals 40, and four sets of shelves 50 are described. May be set.

  The entrance / exit M3 is an entrance / exit of a person entering / exiting the sales floor M2. Further, the entrance / exit M3 includes a camera 20b directed in a direction for capturing an image of the face of a person leaving the sales floor M2. When the camera 20a captures a person's face, the server apparatus 10 determines whether there is already person authentication information. In that case, the processing load of the server apparatus 10 becomes large compared with all the person authentication information that has visited the store. Therefore, by excluding persons who leave the sales floor M2, an increase in processing load can be suppressed. In addition, you may install the camera 20a toward the direction which images the face of the person who enters the sales floor M2 in the entrance / exit M3. This makes it possible to authenticate the faces of all persons who have visited the store.

  Next, each part which the person authentication system 1 of embodiment has is demonstrated.

  First, the hardware configuration of the server device 10 will be described. FIG. 3 is a block diagram illustrating a hardware configuration of the server device 10. The server device 10 is an information processing device that controls the person authentication system 1. As shown in FIG. 3, the server device 10 includes a CPU (Central Processing Unit) 11, a memory 12, a storage unit 13, a network interface 14, a display unit 15, an input unit 16, and a clock unit 17. Prepare.

  The CPU 11 controls the entire server device 10. The memory 12 temporarily stores data. The storage unit 13 is readable and writable by commands from the CPU 11 and stores programs, data, and the like. The storage unit 13 also stores a person authentication program P1 and a person authentication dictionary D.

  The person authentication program P1 is a program for authenticating a person from moving images captured by the cameras 20a and 20b and storing the person authentication information of the authenticated person in the person authentication dictionary D.

  The person authentication dictionary D stores person authentication information for each person authenticated from a moving image captured by the camera 20a in the past. Here, FIG. 4 is an explanatory diagram illustrating an example of a data configuration of the person authentication dictionary D. The person authentication dictionary D has one or more person authentication information associated with the person identification information. The person identification information is identification information assigned to each person and capable of identifying the person. The person authentication information is information related to person authentication extracted from the moving image. The person authentication information may be information related to person authentication extracted from a frame image representing a moving image with the front of the person faced.

  The person authentication information includes a moving image, a feature amount, camera identification information, and an imaging time. The moving image is a moving image obtained by authenticating the person. The moving image may be a frame image representing a moving image in which the front of a person's face is directed. The feature amount is a feature amount of the person extracted from the frame image in which the person is authenticated. Here, the feature amount of a person is a feature amount extracted from a person's face, a feature amount extracted from a person's clothing, or the like. The feature amount of clothes is, for example, the color of clothes worn by a person. The camera identification information is identification information for identifying the camera 20a that captured the moving image. The imaging time is the time when the camera 20a captures a moving image.

  The network interface 14 is connected to the LAN 30. The LAN 30 may be various public networks. The display unit 15 is a display that displays various types of information. The input unit 16 controls information input from an input device such as a keyboard or a mouse. Alternatively, the input unit 16 controls information input from the touch panel stacked on the display unit 15. The clock unit 17 measures time.

  Next, the cameras 20a and 20b will be described. FIG. 5 is a block diagram illustrating a hardware configuration of the cameras 20a and 20b. Although the camera 20a and the camera 20b have different uses, the functions and configurations are the same. Since the camera 20a and the camera 20b have different uses, functions and configurations may be different. Therefore, the camera 20a will be described below as an example.

  The camera 20 a is an imaging device that captures an image of a predetermined area where the camera 20 a is installed and transmits imaging data such as a captured moving image to the server device 10. As shown in FIG. 5, the camera 20 a includes a CPU 21, a memory 22, a storage unit 23, a network interface 24, an imaging unit 25, and a clock unit 26.

  The CPU 21 controls the entire camera 20a. The memory 22 temporarily stores data. The storage unit 23 can be read and written by commands from the CPU 21 and stores programs, data, and the like. The network interface 24 is connected to the LAN 30. The clock unit 26 measures time.

  The imaging unit 25 captures a subject such as a person or an object existing within the imaging range of the camera 20a, and collects color image information. For example, the imaging unit 25 detects light reflected from the subject surface with a light receiving element, and converts visible light into an electrical signal. The light receiving element is, for example, a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD). And the imaging part 25 produces | generates the color image information of 1 frame corresponding to an imaging range by converting the electrical signal into digital data. The color image information for one frame includes, for example, color information in which RGB (Red Green Blue) values are associated with each pixel included in the one frame, and a luminance value for each pixel included in the one frame. And associated luminance information.

  The imaging unit 25 generates a moving image by generating color image information of a plurality of continuous frames from visible light detected one after another. Note that the color image information generated by the imaging unit 25 may be output as a color image in which the RGB values of each pixel are arranged in a bitmap.

  Next, characteristic functions of the person authentication system 1 according to the embodiment will be described. FIG. 6 is a block diagram showing a functional configuration of the person authentication system 1.

  The CPUs 21 of the cameras 20 a and 20 b function as the imaging control unit 211 and the transmission unit 212 by operating according to the control program stored in the memory 22 or the storage unit 23.

  The imaging control unit 211 controls the imaging unit 25 of the cameras 20a and 20b to capture a moving image of a predetermined area where the cameras 20a and 20b are installed. Further, the imaging control unit 211 measures the imaging time when the imaging unit 25 images.

  The transmission unit 212 sequentially transmits the moving image captured by the imaging control unit 211 and the imaging time to the server device 10. At this time, the transmission unit 212 may transmit a moving image for each frame image, or may transmit a moving image for each of a plurality of frame images. Here, the frame image indicates color image information for each frame of a moving image.

  The CPU 11 of the server device 10 operates according to the control program stored in the memory 12 or the storage unit 13, thereby receiving the reception unit 111, the face detection unit 112, the feature amount extraction unit 113, the similarity calculation unit 114, It functions as a similarity determination unit 115, a registration unit 116, and an exclusion unit 117.

  The receiving unit 111 as input means sequentially receives the moving images transmitted from the cameras 20a and 20b and the imaging time. Thereby, the receiving unit 111 receives input of imaging data such as a moving image.

  The face detection unit 112 serving as detection means detects a human face included in the moving image from the moving image received by the receiving unit 111. Further, the face detection unit 112 assigns new person identification information for each detected person. Specifically, the face detection unit 112 obtains coordinates indicating the face area using luminance information of the frame image. Note that any method may be used as a method of obtaining the face area. As an example of the detection method, there is a method of detecting a position that gives the highest correlation value as a face region by obtaining a correlation value while moving a template prepared in advance in input image data. Alternatively, a method using a face extraction method using an eigenspace method or a subspace method may be applied. The shape of the face area to be detected may be any shape, and the shape of the face area may be varied depending on the detection result of the face orientation and size.

  By the way, in the case of moving images captured by the cameras 20a, 20b, etc., it is assumed that the detected face of the same person is continuously shown over a series of consecutive frame images. For this reason, it is necessary to perform tracking processing of a person's face so that they can be associated as the same person. As a method for realizing the tracking process, a method of estimating and associating a position where the face detected using the optical flow exists in the next frame is used. This tracking process is continued until the face area associated with the same person is not detected by the face detection unit 112 and the tracking end is determined.

  The feature amount extraction unit 113, the similarity calculation unit 114, and the similarity determination unit 115 authenticate whether or not the person is registered in the person authentication dictionary D.

  First, the feature quantity extraction unit 113 extracts the feature quantity of the face person detected by the face detection unit 112. More specifically, the feature amount extraction unit 113 detects facial feature points such as eyes, nose, and mouth for each frame image from face areas detected over a plurality of frame images by the tracking process. Then, the feature amount extraction unit 113 extracts a feature amount for each feature point. The feature amount is a parameterization of appearance features such as the shape of feature points, the color of the surface, and unevenness. The feature points of the face to be detected can be arbitrarily changed by setting or the like. In addition, any method may be used as a method for detecting facial feature points in the feature quantity extraction unit 113, and a known technique is used.

  Further, the feature amount extraction unit 113 extracts feature amounts from the person's clothing detected by the face detection unit 112. The feature amount extraction unit 113 extracts a color as a feature amount from clothes such as a person's clothes and accessories. As a result, the clothes of the person are added to the determination as to whether or not the person is registered in the person authentication dictionary D. Therefore, authentication accuracy can be increased.

  The similarity calculation unit 114 compares the feature amount extracted by the feature amount extraction unit 113 with the feature amount for each person registered in the person authentication dictionary D, and calculates the similarity for each person. Here, the similarity indicates how much the two feature quantities are similar by comparing the feature quantity of the person imaged by the cameras 20a and 20b with the feature quantity of each person registered in the person authentication dictionary D. Any value (similarity) may be used.

  The similarity determination unit 115 determines whether or not a person whose similarity is greater than or equal to a threshold is registered in the person authentication dictionary D. More specifically, the similarity determination unit 115 registers the person authentication information of the corresponding person in the person authentication dictionary D when the similarity is equal to or greater than a predetermined threshold among the similarities calculated by the similarity calculation unit 114. It is determined that On the other hand, when the similarity is less than the threshold, the similarity determination unit 115 determines that the person authentication information of the corresponding person is not registered in the person authentication dictionary D. If there are a plurality of pieces of person authentication information equal to or greater than the threshold, the similarity determination unit 115 determines the person with the highest similarity as the person captured by the cameras 20a and 20b.

  When there are a plurality of pieces of person authentication information equal to or greater than the threshold, the similarity determination unit 115 may determine that determination is impossible and exclude it from subsequent processing. When there are a plurality of pieces of person authentication information having a degree of similarity equal to or greater than the threshold, the moving images captured by the cameras 20a and 20b may be unclear. By excluding unclear moving images with low reliability, the similarity determination unit 115 can ensure reliability. Further, it is assumed that an arbitrary value can be set as the predetermined threshold.

  The registration unit 116 registers the person authentication information related to the moving image captured by the camera 20a in the person authentication dictionary D. More specifically, the registration unit 116 captures the moving image from which the similarity is determined, the feature amount extracted by the feature amount extraction unit 113, the identification information of the camera 20a that transmitted the moving image, and the camera 20a. The imaging time is registered in the person authentication dictionary D as person authentication information.

  The registration performed by the registration unit 116 as a registration unit includes new registration and additional registration. When the similarity determination unit 115 determines that person authentication information of a person equal to or greater than the threshold is not registered in the person authentication dictionary D, the registration unit 116 uses the new person identification information assigned by the face detection unit 112. The person authentication information is newly registered in the person authentication dictionary D.

  On the other hand, when the similarity determination unit 115 determines that person authentication information of a person with a threshold value or more is registered in the person authentication dictionary D, the registration unit 116 adds the person authentication information having the person identification information of the corresponding person. The person authentication information related to the current imaging is additionally registered in association with each other. As described above, the registration unit 116 performs additional registration to increase the person authentication information of the corresponding person identification information. Thereby, since the similarity calculation part 114 can compare with several person authentication information, it can improve the reliability of a determination result.

  The exclusion unit 117 serving as an exclusion unit excludes a predetermined person in the person authentication dictionary D from the target of the similarity calculation unit 114. Thereby, the similarity is not calculated for the excluded person. Therefore, the exclusion unit 117 can reduce the processing load. When the person authentication information corresponding to the person included in the moving image captured by the camera 20b is registered in the person authentication dictionary D, or when there is person authentication information after a predetermined time, the excluding unit 117 has a similarity degree. Are excluded from the calculation target.

  First, a case where person authentication information corresponding to a person included in a moving image captured by the camera 20b is registered in the person authentication dictionary D will be described. The camera 20a does not capture a person who is not in the sales floor M2. Therefore, the exclusion unit 117 can reduce the processing load on the server device 10 by excluding persons who leave the sales floor M2. Here, referring back to FIG. 2, the camera 20b is directed in a direction to image the face of a person leaving the sales floor M2. Therefore, the camera 20b can capture an image of a person leaving the sales floor M2. Then, the exclusion unit 117 detects the person's face included in the captured moving image, extracts the feature amount, calculates the similarity, and determines the similarity to determine the retired person. Exclude from degree calculation target.

  Next, a case where there is person authentication information for which a predetermined time has elapsed will be described. The excluding unit 117 excludes the person whose personal authentication information has passed a predetermined time from the similarity calculation target. More specifically, there is a limit to the time for a person to travel around the sales floor M2. Therefore, since the exclusion unit 117 is considered not to exist in the sales floor M2, the exclusion unit 117 excludes the person whose personal authentication information has passed a predetermined time from the similarity calculation target. Accordingly, the exclusion unit 117 can exclude from the similarity calculation target even when the camera 20b cannot capture the face of the person leaving the sales floor M2. Alternatively, even if the exclusion unit 117 can capture an image but cannot determine that the person is included in the person authentication dictionary D, it can be excluded from the similarity calculation target. The starting point for the predetermined time is not limited. An example is when the person authentication information is first registered in the person authentication dictionary D or when the person authentication information is registered last in the person authentication dictionary D.

  Next, a person authentication process executed by the CPU 11 of the server device 10 according to the above-described embodiment according to the control program will be described. The server apparatus 10 according to the embodiment executes person authentication processing using a moving image captured by the camera 20a.

  FIG. 7 is a flowchart showing the flow of person authentication processing executed by the CPU 11 of the server device 10 according to the control program.

  First, the CPU 11 (reception unit 111) of the server device 10 receives a moving image captured by the camera 20a (step S11).

  Next, the CPU 11 (face detection unit 112) of the server device 10 determines whether or not a face can be detected from the received moving image (step S12). When a face cannot be detected (step S12; No), the CPU 11 (reception unit 111) of the server device 10 returns to step S11 and receives a moving image.

  On the other hand, when the face can be detected (step S12; Yes), the CPU 11 (face detection unit 112) of the server device 10 assigns new person identification information to the detected face (step S13).

  Next, the CPU 11 (reception unit 111) of the server device 10 stores the received moving image (step S14). Next, the CPU 11 (face detection unit 112) of the server device 10 determines whether or not the face detected from the received moving image is out of frame (step S15).

  When the detected face is not out of frame (step S15; No), the CPU 11 of the server device 10 returns to step S14 and stores the received moving image.

  On the other hand, when the detected face is out of frame (step S15; Yes), the CPU 11 (feature amount extraction unit 113, similarity calculation unit 114, similarity determination unit 115) of the server device 10 detects the person of the detected face. Is an authentication process for determining whether or not the same person is registered in the person authentication dictionary D (step S16). More specifically, the CPU 11 of the server device 10 extracts feature amounts from the detected face and clothing. The CPU 11 of the server device 10 compares the extracted feature quantity with the feature quantity of the person authentication information registered in the person authentication dictionary D to calculate the similarity. Then, the CPU 11 of the server device 10 authenticates whether or not the person is registered in the person authentication dictionary D based on the calculated similarity.

  Next, the CPU 11 of the server device 10 determines whether or not the same person as the detected face person has been registered in the person authentication dictionary D by person authentication (step S17). When it is authenticated that the same person as the detected face person is registered in the person authentication dictionary D (step S17; Yes), the CPU 11 (registration unit 116) of the server device 10 corresponds to the person authentication dictionary D. The person authentication information is additionally registered in the person identification information to be executed (step S18).

  On the other hand, when it is not authenticated that the same person as the detected face person is registered in the person authentication dictionary D (step S17; No), the CPU 11 (registration unit 116) of the server device 10 in step S13. The person authentication information is newly registered in the person authentication dictionary D using the assigned person identification information (step S19).

  As described above, the CPU 11 of the server device 10 ends the person authentication process.

  Next, an exclusion process executed by the CPU 11 of the server device 10 according to the above-described embodiment according to the control program will be described.

  FIG. 8 is a flowchart showing the flow of exclusion processing executed by the CPU 11 of the server device 10 according to the control program.

  First, the CPU 11 (exclusion unit 117) of the server device 10 determines whether or not the person authentication information registered in the person authentication dictionary D has passed a predetermined time (step S21). When the predetermined time has passed (step S21; Yes), the CPU 11 (exclusion unit 117) of the server device 10 excludes the person of the corresponding person authentication information from the similarity calculation target (step S22).

  On the other hand, if the predetermined time has not elapsed (step S21; No), can the CPU 11 (face detection unit 112) of the server device 10 detect the face from the moving image captured by the camera 20b and received? It is determined whether or not (step S23).

  When the face is not detected from the moving image captured by the camera 20b and received (step S23; No), the CPU 11 of the server device 10 returns to step S21.

  On the other hand, when a face is detected from the received moving image captured by the camera 20b (step S23; Yes), the CPU 11 of the server device 10 (feature amount extraction unit 113, similarity calculation unit 114, similarity determination unit 115). Performs an authentication process as to whether or not the detected face person is the same person registered in the person authentication dictionary D (step S24). More specifically, the CPU 11 of the server device 10 extracts the detected face and clothing feature quantities. The CPU 11 of the server device 10 compares the extracted feature quantity with the feature quantity of the person authentication information registered in the person authentication dictionary D to calculate the similarity. Then, the CPU 11 of the server device 10 authenticates whether or not the person is registered in the person authentication dictionary D based on the calculated similarity.

  Next, the CPU 11 (similarity determination unit 115) of the server device 10 determines whether or not it is authenticated by person authentication that the same person as the detected face person is registered in the person authentication dictionary D (step). S25). When it is not authenticated that the same person as the detected face person is registered in the person authentication dictionary D (step S25; No), the CPU 11 of the server device 10 does not exclude the person identification information, and continues. The erasing process ends.

  On the other hand, when it is authenticated that the same person as the detected face person is registered in the person authentication dictionary D (step S25; Yes), the CPU 11 (exclusion unit 117) of the server device 10 performs the corresponding person authentication. The person of information is excluded from the authentication process (step S22).

  Thus, the CPU 11 of the server device 10 ends the exclusion process.

  As described above, according to the server device 10 of the present embodiment, the CPU 11 of the server device 10 receives the moving image captured by the camera 20a and detects a human face included in the moving image. Then, the CPU 11 of the server device 10 authenticates whether or not the detected person is a person registered in the person authentication dictionary D in which person authentication information of a person included in a moving image captured in the past is registered. . When the CPU 11 of the server device 10 authenticates that the detected person is a person registered in the person authentication dictionary D, the CPU 11 additionally registers the person authentication information extracted from the moving image in the person authentication dictionary D. Therefore, the CPU 11 of the server device 10 can determine that the person who has been framed out and then framed in again is the same person.

  In addition, in the person authentication information of the person authentication dictionary D, camera identification information that can identify the captured camera 20a and an imaging time are recorded. And even if it is a case where it is a case where it picturizes with a plurality of cameras 20a, in the case of the same person, person authentication information is registered in association with the same person identification information of person authentication dictionary D. Accordingly, it is possible to track the action history of a person.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  In the above-described embodiment, the server device 10 accepts the input of a moving image when the receiving unit 111 receives the moving images sequentially captured by the cameras 20a and 20b. However, the moving image input method is not limited to this. The moving image may be a moving image file captured by the cameras 20a and 20b in the past.

  The program executed by each device of the above-described embodiment or modification is provided by being incorporated in advance in a storage medium (ROM or storage unit) included in each device, but is not limited thereto. For example, an installable or executable file is recorded on a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, or a DVD (Digital Versatile Disk). You may comprise. Furthermore, the storage medium is not limited to a medium independent of a computer or an embedded system, but also includes a storage medium in which a program transmitted via a LAN, the Internet, or the like is downloaded and stored or temporarily stored.

  Further, the program executed by each device of the above-described embodiment or modification may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network, or the Internet It may be configured to be provided or distributed via a network.

DESCRIPTION OF SYMBOLS 1 Person authentication system 10 Server apparatus 11 CPU
DESCRIPTION OF SYMBOLS 111 Reception part 112 Face detection part 113 Feature-value extraction part 114 Similarity calculation part 115 Similarity determination part 116 Registration part 117 Exclusion part 13 Memory | storage part D Person authentication dictionary 20a, 20b Camera 21 CPU
211 Imaging control unit 212 Transmission unit

JP2013-65119A

Claims (5)

Input means for receiving input of imaging data captured by the imaging unit;
Detecting means for detecting a person from imaging data received by the input means;
Authentication means for authenticating whether the person detected by the detection means is a person registered in a dictionary in which a person included in imaging data captured by the imaging unit in the past is registered;
A registration unit for additionally registering the imaging data captured by the imaging unit to the person in the dictionary when the authentication unit authenticates the person registered in advance;
Exclusion of excluding a person included in the image data from the object of the authentication means on condition that the imaging data received by the input means is image data captured by an imaging unit that captures a person who leaves Means,
An information processing apparatus comprising: The registration unit newly registers the imaging data captured by the imaging unit when it is determined that the authentication unit is not registered in the dictionary.
The information processing apparatus according to claim 1. The authentication means authenticates the person's clothes detected by the detection means by comparing the person's clothes registered in the dictionary.
The information processing apparatus according to claim 1 or 2. The excluding unit excludes a person who has passed a predetermined time from the registration unit registering the imaging data from the object of the authentication unit;
The information processing apparatus according to any one of claims 1 to 3 . Computer
Input means for receiving input of imaging data captured by the imaging unit;
Detecting means for detecting a person from imaging data received by the input means;
Authentication means for authenticating whether or not the person detected by the detection means is a person registered in a dictionary in which a person included in imaging data captured by the imaging unit in the past is registered;
A registration unit for additionally registering the imaging data captured by the imaging unit to the person in the dictionary when the authentication unit authenticates the person registered in advance;
Exclusion of excluding a person included in the image data from the object of the authentication means on condition that the imaging data received by the input means is image data captured by an imaging unit that captures a person who leaves Means,
Program to function as.

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