Patent Description:
In recent years, in a situation of identity verification, biometrics recognition has been used in which biometrics information that is information on a physical feature or an action feature of a human is used for authentication. Face recognition, which is one type of biometrics recognition, has advantages of less psychological resistance at an authentication object, a capability of performing authentication even from a distant place, a psychological deterrence effect against dishonesty, and the like.

Face recognition is a technology to compare information obtained from a face image of a person captured by a camera with information on a registered person and authenticate whether or not the captured person is the same person as the registered person. Patent Literature <NUM> and Patent Literature <NUM> disclose a face recognition scheme using an infrared image as a technology of preventing a so-called impersonate act that is to attempt to pass through face recognition using a non-living body such as a photograph. Since a print such as a photograph has a property that does not absorb infrared light, it is possible to determine whether an object is a living body or a photograph from the captured infrared image and exclude a dishonest act.

The Japanese patent application <CIT> discloses a face image recognizing apparatus capable of recognizing a face image with high reliability at a high speed. Imaging means capture an object to generate infrared ray image data and visible light image data. Face position specifying means specify a region corresponding to a face image of a person based on the infrared ray image data. Face pattern detection means maps the face image region into the visible light image and detects whether or not the image data in the face image region correspond to the face pattern of a registered person.

<NPL>, discloses a sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF). In order to fuse sensor data, registration is used to transform different sets of data into one coordinate system. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed.

When a person having a deep colored skin such as a black person is captured by an infrared camera, however, a reflected infrared light cannot be sufficiently detected due to a large absorption rate of the infrared light, and it may be difficult to determine whether or not a living body is captured. Further, when face recognition is performed from an infrared image, the accuracy of face recognition is lower compared to a case where face recognition is performed from a visible light image.

One example object of the present invention is to provide an image processing device, an image processing method, a face recognition system, a program, and a storage medium that can accurately determine a living body and non-living body from a captured image and extract a face image of a living body suitable for face recognition.

According to one example aspect of the present invention, provided is an image processing device according to claim <NUM>.

Further, according to yet another example aspect of the present invention, provided is an image processing method according to claim <NUM>.

According to the present invention, it is possible to accurately determine a living body and non-living body from a captured image and extract a face image of a living body suitable for face recognition.

A face recognition system according to an embodiment of the present invention will be described with reference to <FIG> is a block diagram illustrating a general configuration of the face recognition system according to the embodiment.

As illustrated in <FIG>, a face recognition system <NUM> according to the embodiment includes a capture unit <NUM>, an image processing unit <NUM>, a face recognition unit <NUM>, a storage unit <NUM>, and a control unit <NUM>.

The capture unit <NUM> acquires a first image generated by capturing an object by using a light at a first wavelength, a second image generated by capturing the object by using a light at a second wavelength, and depth information (three-dimensional information) of the object. The light at the first wavelength is a visible light, for example. The light at the second wavelength is an infrared light, for example. The capture unit <NUM> may include a camera <NUM> that captures a visible light image, a camera <NUM> that captures a near-infrared image, and a projector <NUM> that irradiates an object with a laser light of a particular pattern, for example. In the embodiment, a case where a visible light image is captured by the camera <NUM> and a near-infrared image is captured by the camera <NUM> will be described as an example.

A method of acquiring depth information on an object is not particularly limited. For example, in the example of using the capture unit <NUM> in the configuration described above, a method of performing capturing by using the camera <NUM> in a state where a laser light of a particular pattern is irradiated by the projector <NUM> can be applied. By analyzing a pattern of a reflected light of a laser light of the particular pattern from a captured image, it is possible to measure the distance to an object. Alternatively, a method of measuring the distance to an object based on a parallax of images acquired by a plurality of cameras or a method of measuring the distance to an object by irradiating an object with an infrared laser and using a time difference of a received reflected light (TOF scheme) may be used.

A commercially available product having the same function as the capture unit <NUM> of the configuration described above may be, for example, Kinect (registered trademark) from Microsoft Corporation, RealSense (registered trademark) from Intel Corporation, or the like. These products have an RGB camera as a unit that captures a visible light image and have a near-infrared projector and a near-infrared camera as a unit that captures an image including depth information on an object.

The image processing unit <NUM> is a function block that extracts, from images captured by the capture unit <NUM>, a face image in which a living body is captured and includes an image data acquisition unit <NUM>, a face detection unit <NUM>, a liveness check unit <NUM>, and a face image extraction unit <NUM>.

The image data acquisition unit <NUM> acquires, from the capture unit <NUM>, visible light image data captured by the camera <NUM>, near-infrared image data captured by the camera <NUM>, and depth information on an object. The face detection unit <NUM> detects a face included in a visible light image and a near-infrared image acquired by the image data acquisition unit <NUM>. The liveness check unit <NUM> checks whether or not a face image in the near-infrared image detected by the face detection unit <NUM> is an image obtained by capturing living body based on the depth information on an object acquired by the image data acquisition unit <NUM>. The face image extraction unit <NUM> extracts a face image used for face recognition from a visible light image based on information on a face included in the near-infrared image.

The face recognition unit <NUM> is a function block that determines whether or not a person of a face image extracted by the image processing unit <NUM> matches a pre-registered person and includes a face feature amount extraction unit <NUM> and a matching unit <NUM>. Note that, while the image processing unit <NUM> and the face recognition unit <NUM> are separated here for simplified illustration, the image processing unit <NUM> may have a function of a part of the face recognition unit <NUM>.

The face feature amount extraction unit <NUM> extracts, from a face image of a visible light image extracted by the face image extraction unit <NUM>, a face feature amount that is a parameter representing a feature of a face. The matching unit <NUM> performs a matching process to compare a face feature amount of a face image of the visible light image extracted by the face feature amount extraction unit <NUM> with a face feature amount of a face image of a person registered in the storage unit <NUM>.

The control unit <NUM> is a control unit responsible for the entire process in the capture unit <NUM>, the image processing unit <NUM>, and a face recognition unit <NUM>.

Next, a face recognition method using the face recognition system <NUM> will be specifically described by using <FIG> is a flowchart illustrating the face recognition method in the face recognition system.

Some commercially available capture devices in which a visible light camera and a near-infrared camera are integrated have a coordinate conversion function that associates coordinates of an image captured by the visible light camera with coordinates of an image captured by the near-infrared camera. An image processing method using such a coordinate conversion function will be described. Note that a visible light camera and a near-infrared camera formed as separated devices may be used, and a coordinate conversion function that associates coordinates of an image captured by the visible light camera with coordinates of an image captured by the near-infrared camera may be separately prepared.

The face recognition process using the face recognition system <NUM> is performed in accordance with step S101 to step S107 of <FIG>. Note that a process of each unit described later is performed under the control by the control unit <NUM>.

First, the capture unit <NUM> captures an object as a visible light image by using the camera <NUM> and captures the object as a near-infrared image by using the camera <NUM>. Further, the capture unit <NUM> acquires depth information on the object by using the camera <NUM> and the projector <NUM> (step S101). It is desirable that a visible light image and an infrared image be captured in synchronization. The captured visible light image and the captured near-infrared image are images in which at least the same single person is an object. The depth information on an object is generated by using information on a near-infrared light reflected at the object.

The image data of the visible light image and the image data of the near-infrared image captured by the capture unit <NUM> are transmitted to the image data acquisition unit <NUM> of the image processing unit <NUM> together with the depth information on the object. Note that, in the image data acquisition unit <NUM>, additional image processing such as a correction process may be performed on the received image data, if necessary.

Next, the face detection unit <NUM> performs a face detection process to detect a face included in an image on the near-infrared image transmitted to the image data acquisition unit <NUM> (step S102). An algorithm used for face detection by the face detection unit <NUM> is not particularly limited, and various algorithms can be applied.

Next, the liveness check unit <NUM> checks whether or not the face image detected from the near-infrared image is an image acquired by the camera <NUM> capturing a living body (step S103). Specifically, the liveness check unit <NUM> references the depth information on the object acquired by the capture unit <NUM> and determines whether or not depth information in accordance with unevenness representing a feature of a living body is included in a face part detected from the near-infrared image.

For example, since a living body includes depth information different for positions in accordance with unevenness of a face, when a distribution of depth information is recognized in a face part, the detected face image can be determined as an image obtained by capturing a living body. On the other hand, since a two-dimensional image such as a photograph does not include depth information that is different for positions in accordance with unevenness of a face, when the depth information on a face part is even, the detected face image can be determined as not obtained by capturing a living body.

In step S103, if it is determined by the liveness check unit <NUM> that the detected face image is an image obtained by capturing a living body ("YES" in the figure), the process proceeds to step S104. On the other hand, in step S103, if it is determined by the liveness check unit <NUM> that the detected face image is not an image obtained by capturing a living body ("NO" in the figure), it is determined that face recognition failed, and the process returns to step S101. Note that a case where the detected face is determined as not obtained by capturing a living body includes a case where a face is not detected from the near-infrared image in step S102.

Next, the face image extraction unit <NUM> uses a coordinate conversion function included in a capture device to convert coordinates on the near-infrared image of the face image determined as obtained by capturing a living body into coordinates on a visible light image (step S104). With a use of the coordinate conversion function provided in the capture device, it is possible to easily determine association to which person's face included in a visible light image the face in the near-infrared image determined as obtained by capturing a living body corresponds.

Next, the face image extraction unit <NUM> extracts a corresponding face image from the visible light image based on coordinates on the visible light image of the face determined as obtained by capturing a living body (step S105). That is, the face image extraction unit <NUM> uses information on a face included in the near-infrared image, namely, information on the position of the face included in the near-infrared image to extract the face image corresponding to a face included in the near-infrared image from the visible light image. At this time, it is desirable to cut out a slightly wide image taking into consideration of a conversion error from coordinates on the near-infrared image to coordinates on the visible light image.

In such a way, only the face image obtained by capturing a living body can be extracted from a captured visible light image.

Next, the face recognition unit <NUM> performs a face recognition process by using the face image extracted from the visible light image by the image processing unit <NUM>. By using a visible light image to perform face recognition, it is possible to improve the accuracy of face recognition compared to the case of using a near-infrared image.

First, the face feature amount extraction unit <NUM> extracts a face feature amount, which is a parameter representing a feature of a face, from the face image extracted in step S105 (step S106). The extraction of a face feature amount may be performed after face detection is performed by the face detection unit <NUM> on the face image cut out from the visible light image.

The face feature amount is a vector amount, which is a combination of components of scalar amounts each representing a feature of a face image. The component of a feature amount is not particularly limited, and various types of components can be used. For example, as a component of a feature amount, a positional relationship such as a distance or an angle between feature points set at the center or the end point of an organ such as an eye, a nose, a mouth, or the like, a curvature of the outline of a face, a color distribution or a value of light and shade of the surface of a face, or the like can be used. The number of components of the feature amount is not particularly limited and can be suitably set in accordance with a required recognition accuracy, a processing speed, or the like.

Next, the matching unit <NUM> performs a matching process to match whether or not the person in the face image extracted from the visible light image matches any of the persons registered in the storage unit <NUM> (step S107). Specifically, the matching unit <NUM> compares the face feature amount of the face image extracted in step S106 with the face feature amount of the face image of a person registered in the storage unit <NUM>. When a person whose similarity of the face feature amount exceeds a predetermined threshold value exists in the storage unit <NUM>, the matching unit <NUM> determines that the person of the face image extracted from the visible light image is the same person as a person who is already registered, that is, determines that the face recognition succeeded. On the other hand, when a person whose similarity of the face feature amount exceeds a predetermined threshold value does not exist in the storage unit <NUM>, the matching unit <NUM> determines that the person of the face image extracted from the visible light image is a person who is not registered in the storage unit <NUM>, that is, determines that the face recognition failed.

The control unit <NUM> then performs a predetermined process in accordance with a result of the reference process. For example, when the face recognition system is used for a gate system of immigration control, access control for a room, or the like, the gate system is controlled so that entry is allowed only when face recognition is successful.

When the similarity of the face feature amount is less than a predetermined threshold value, the face recognition system <NUM> may determine this as dishonesty and issue an alert. Further, when a detected face is determined as not obtained by capturing a living body despite the fact that the face is detected in the near-infrared image, there is a likelihood that dishonesty to attempt to pass through the gate system by using a photograph or the like is performed. This may be determined as dishonesty, and an alert may be issued in such a case. In contrast, when the storage unit <NUM> stores a face feature amount of a face image of a person included in a blacklist, the face recognition system <NUM> may issue an alert when the authentication is successful.

As discussed above, it is possible to accurately determine a living body and a non-living body from a captured image and extract a face image of a living body suitable for face recognition. Thereby, a face recognition system capable of implementing accurate face recognition can be realized.

A face recognition system and a face recognition method will be described with reference to <FIG>. The same components as those in the face recognition system according to the embodiment illustrated in <FIG> are labeled with the same references, and the description thereof will be omitted or simplified.

First, a general configuration of the face recognition system will be described by using <FIG> is a block diagram illustrating the general configuration of the face recognition system.

As illustrated in <FIG>, the face recognition system <NUM> is the same as the face recognition system according to the embodiment illustrated in <FIG> except that the image processing unit <NUM> further includes a face number determination unit <NUM>. The face number determination unit <NUM> determines the number of faces included in a visible light image and the number of faces determined as obtained by capturing living bodies out of faces included in a near-infrared image.

In the face recognition system of <FIG>, association of a visible light image and a near-infrared image is performed based on the number of faces included in the visible light image and the number of faces determined as obtained by capturing living bodies out of faces included in the near-infrared image instead of a coordinate conversion function. Therefore, the face recognition system is not necessarily required to use a capture device having a coordinate conversion function between a visible light image and a near-infrared image.

Next, the face recognition method using the face recognition system <NUM> will be specifically described by using <FIG>. <FIG> is a flowchart illustrating the face recognition method in the face recognition system according to <FIG>. <FIG> are diagrams illustrating an example of erroneous extraction of a face image when an object including a two-dimensional face image is captured. <FIG> illustrates a near-infrared image, and <FIG> illustrates a visible light image.

The face recognition process using the face recognition system <NUM> according to <FIG> is performed in accordance with step S201 to step S207 of <FIG>. Note that a process of each unit described later is performed under the control by the control unit <NUM>.

First, the capture unit <NUM> captures an object as a visible light image by using the camera <NUM> and captures the object as a near-infrared image by using the camera <NUM>. Further, the capture unit <NUM> acquires depth information on the object by using the camera <NUM> and the projector <NUM> (step S201). Since the coordinate conversion function is not used, while the camera <NUM> and the camera <NUM> may be separate capture devices, it is desirable to perform calibration in advance so that the field of views of the cameras <NUM> and <NUM> are substantially the same. The image data of the visible light image and the image data of the near-infrared image captured by the capture unit <NUM> are transmitted to the image data acquisition unit <NUM> of the image processing unit <NUM> together with the depth information on the object.

Next, the face detection unit <NUM> performs a face detection process to detect a face included in an image on the visible light image and the near-infrared image transmitted to the image data acquisition unit <NUM>, respectively (step S202).

Next, the liveness check unit <NUM> checks whether or not the face image detected from the near-infrared image is an image acquired by the camera <NUM> capturing a living body (step S203). If a plurality of faces are detected from the near-infrared image, the liveness check unit <NUM> checks whether or not each of the face images is an image acquired by capturing a living body. The method of the determination is the same as that in step S103.

Next, the face number determination unit <NUM> determines the number of faces detected from the visible light image and the number of faces detected from the near-infrared image and determined as obtained by capturing the face of living bodies, and determines whether or not both the numbers are the same (step S204).

If the face number determination unit <NUM> determines that the numbers of faces are the same in the sept S204 ("YES" in the figure), the face number determination unit <NUM> determines that all the face images detected in the visible light image are images obtained by capturing living bodies, and the process proceeds to step S205. On the other hand, if the face number determination unit <NUM> determines that the numbers of faces are different from each other in the step S204 ("NO" in the figure), the face number determination unit <NUM> determines that at least some of the faces detected in the visible light image are images obtained by capturing a two-dimensional image such as a photograph, and the process returns to step S201. In such a case, an alert may be issued indicating the presence of a dishonest act. Furthermore, a face detected from only the visible light image may be identified. Further, the face number determination unit <NUM> may output a face image of a face detected from only the visible light image as a face obtained by a dishonest act.

Next, the face image extraction unit <NUM> extracts an image of a face (face image) detected from the visible light image (step S205). This step is performed when the number of faces detected from the visible light image and the number of faces detected from the near-infrared image and determined as obtained by capturing the face of living bodies are the same. That is, in this step, information on a face included in the near-infrared image, namely, the number of faces detected from the near-infrared image and determined as obtained by capturing the face of living bodies is used to extract a face image corresponding to a face included in the near-infrared image from the visible light image. Note that, when a face image is extracted from a visible light image, the coordinate conversion function described in the face recognition process illustrated in <FIG> may be used to extract a face image corresponding to a face included in the near-infrared image from the visible light image.

Next, the face feature amount extraction unit <NUM> extracts a face feature amount, which is a parameter representing a face feature, from the face image extracted from the visible light image in step S205 (step S206). The extraction method of a face feature amount is the same as that in step S106.

Next, the matching unit <NUM> performs a matching process to match whether or not the person in the face image extracted from the visible light image matches any of the persons registered in the storage unit <NUM> (step S207). The method of a matching process is the same as that in step S107.

The control unit <NUM> then performs a predetermined process in accordance with a result of the matching process.

As described above, the detected face image is used as a target of a face recognition process only when the number of faces detected from the visible light image and the number of faces detected from the near-infrared image and determined as obtained by capturing the face of living bodies are the same. The reason for this will be described below.

It is possible to employ an operation in which, when even one image which is determined as not acquired by the camera <NUM> capturing a living body is present in face images detected from a near-infrared image, it is immediately determined that a dishonest act using a two-dimensional image such as a photograph is performed.

However, for example, when a poster or the like including a human's face is happened to be attached on a wall within a field of view of a camera, a failure such as being unable to enter a face recognition process may occur.

Further, when there is an object which can be detected as a face in a visible light image but is less likely to be detected as a face in a near-infrared image, such as a photograph of a white face, for example, a two-dimensional face image may not be excluded by only the liveness check on the near-infrared image.

For example, as illustrated in <FIG>, in a near-infrared image of an object including a person A and a photograph of a person B, it is assumed that the face of the person A has been detected, but the face of the person B has not been detected. In this case, since no liveness check is performed on the face image of the person B, it will be determined that no dishonest act using a two-dimensional image is performed. Then, in a subsequent face recognition process, as illustrated in <FIG>, the face of the person A and the face of the person B included in a visible light image are determined to be process targets, and face recognition will be performed thereon, respectively.

As discussed above, when there is an object which is detected as a face in a visible light image but is less likely to be detected as a face in a near-infrared image, the number of faces detected from the visible light image and the number of faces detected from the near-infrared image will be different from each other.

Accordingly, comparison between the number of faces detected in a visible light image and the number of faces detected in a near-infrared image is performed in addition to liveness check on a near-infrared image. When a two-dimensional image that can be detected as a face from a near-infrared image is present in the field of view of a camera, this face image can be excluded by using liveness check. When a two-dimensional image that is unable to be detected as a face from a near-infrared image is present in the field of view of a camera, the presence of this face image can be recognized based on the fact that the number of faces detected in the visible light image and the number of faces detected in the near-infrared image are different from each other. Thereby, a face image to be included in a target of a face recognition process can be appropriately selected out of captured images to perform a face recognition process.

As discussed above, it is possible to accurately determine a living body and a non-living body from a captured image and extract a face image of a living body suitable for face recognition without converting coordinates of a visible light image into coordinates of a near-infrared image. Thereby, a face recognition system capable of implementing accurate face recognition can be realized.

A face recognition method according to an embodiment of the present invention will be described with reference to <FIG>. The same components as those in the face recognition system according to <FIG> and <FIG> are labeled with the same references, and the description thereof will be omitted or simplified.

<FIG> is a flowchart illustrating the face recognition method in the face recognition system according to the present embodiment. <FIG> is a diagram illustrating an example of a visible light image captured by the capture unit. <FIG> is a diagram illustrating an example of a near-infrared image captured by the capture unit. <FIG> is a diagram illustrating an example of face recognition result when a face recognition process of the present embodiment is performed based on images in <FIG>.

In the present embodiment, another face recognition method using the face recognition system according to <FIG> and <FIG> will be described. The face recognition method of the present embodiment associates an image captured by the visible light camera with an image captured by a near-infrared camera by using face recognition. Also in the present embodiment, the coordinate conversion function is not used.

The face recognition process according to the present embodiment is performed in accordance with step S301 to step S307 of <FIG>. Note that a process of each unit described later is performed under the control by the control unit <NUM>.

First, the capture unit <NUM> captures an object as a visible light image by using the camera <NUM> and captures the object as a near-infrared image by using the camera <NUM>. Further, the capture unit <NUM> acquires depth information on the object by using the camera <NUM> and the projector <NUM> (step S301). The image data of the visible light image and the image data of the near-infrared image captured by the capture unit <NUM> are transmitted to the image data acquisition unit <NUM> of the image processing unit <NUM> together with the depth information on the object.

Next, the face detection unit <NUM> performs a face detection process to detect a face included in an image on the visible light image and the near-infrared image transmitted to the image data acquisition unit <NUM>, respectively (step S302).

A case where a person A, a person C, and a photograph of a person B are present in the field of views of the cameras <NUM> and <NUM> is assumed here as an example. Further, a face of a person A', a face of a person B', and a face of a person C' are detected from a visible light image, for example, as illustrated in <FIG>. Further, a face of a person A", a face of a person B", and a face of a person C" are detected from a near-infrared image, for example, as illustrated in <FIG>. It is here assumed that the person A and the person B are persons who are already registered in the storage unit <NUM>, and the person C is a person who is not yet registered in the storage unit <NUM>.

Next, the liveness check unit <NUM> checks whether or not a face detected from the near-infrared image is an image obtained by capturing a living body (step S303). The method of the determination is the same as that in step S103.

In the example of <FIG>, the face of the person A" and the face of the person C" are determined as images obtained by capturing living bodies, and the face of the person B" is determined as an image obtained by capturing a photograph. Thereby, the face of the person B" is excluded from a group of faces detected from the near-infrared image.

Next, the face image extraction unit <NUM> extracts an image of a face (face image) detected from the visible light image and a face image determined as detected from the near-infrared image and obtained by capturing a living body (hereafter, referred to as a "biometric face image") (step S304).

Next, the face feature amount extraction unit <NUM> extracts a face feature amount, which is a parameter representing a feature of a face, from each of the face image detected from the visible light image and the biometric face image detected from the near-infrared image (step S305). The extraction method of a face feature amount is the same as that in step S106.

Next, the matching unit <NUM> determines whether or not a person of the face image detected from the visible light image is the same as any of persons in the biometric face image detected from the near-infrared image (step S306). Specifically, for all the combinations of the face image detected from the visible light image and the biometric face image detected from the near-infrared image, the matching unit <NUM> compares the face feature amount of the face image detected from the visible light image with the face feature amount of the biometric face image detected from the near-infrared image. The matching unit <NUM> then determines a face image to be extracted from the visible light image based on the comparison between the face feature amount of the face image detected from the visible light image and the face feature amount of the biometric face image detected from the near-infrared image. For example, the matching unit <NUM> determines that the combination in which the similarity between the face feature amounts exceeds a predetermined threshold value is from the same person.

Typically, the number of face images detected from a visible light image is the maximum face detection number. Further, the number of biometric face images detected from a near-infrared image is less than or equal to the maximum face detection number. When the maximum face detection number is N, matching to face images of at most N persons will be performed for a single person. When a plurality of persons whose similarity degree of the face feature amount exceeds a predetermined threshold value are present, a person having the face image of the highest score can be determined as the same person out of the plurality of persons.

If the matching unit <NUM> determines that at least some of the persons of face images detected from the visible light image match persons of the biometric face image detected from the near-infrared image ("YES" in the figure), the process proceeds to step S307. A face image of the visible light image for which a face image of the same person is not found in the near-infrared image is excluded from a group of the face images detected from the visible light image. In this sense, it can be said that the matching unit <NUM> is an extraction unit that, out of all the face images of the persons included in the visible light image, extracts one or more face images of the persons included as biometric face images in the near-infrared image.

On the other hand, if the matching unit <NUM> determines that none of the persons of face images detected from the visible light image matches a person of the biometric face image detected from the near-infrared image ("NO" in the figure), it is determined that the face recognition failed, and the process returns to step S301.

In the example of <FIG>, it is determined that the face image of the person A' in the visible light image and the face image of the person A" in the near-infrared image are images obtained by capturing the same person A. Further, it is determined that the face image of the person C' in the visible light image and the face image of the person C" in the near-infrared image are images obtained by capturing the same person C. On the other hand, since the face image of the person B" is excluded from the group of face images detected from the near-infrared image, it is determined that a person corresponding to the person B' in the visible light image is not included in the near-infrared image.

Note that the liveness check in step S303 may be performed on and after step S306. In this case, however, since it is necessary to perform a process such as extraction of a face feature amount also on a face image obtained by capturing a two-dimensional image, it is desirable to perform the liveness check before the step S305 in terms of reduction in the processing load.

Next, the matching unit <NUM> determines whether or not the person of the face image extracted from the visible light image matches any of the persons registered in the storage unit <NUM> (step S307). Specifically, the matching unit <NUM> compares a face feature amount of the face image extracted from the visible light image with a face feature amount of the face image of a person registered in the storage unit <NUM> for each of the face images extracted from the visible light image. If a face image of a person whose similarity of the face feature amount exceeds a predetermined threshold value for each of the face images extracted from the visible light image is present in the storage unit <NUM>, the matching unit <NUM> determines that the person of the face image extracted from the visible light image is the same as a person who is already registered, that is, determines that the face recognition succeeded. On the other hand, if a face image of a person whose similarity of the face feature amount exceeds a predetermined threshold is not present in the storage unit <NUM>, the matching unit <NUM> determines that the person of the face image extracted from the visible light image is a person who is not registered in the storage unit <NUM>, that is, determines that the face recognition failed.

If the face recognition is successful in step S307 ("YES" in the drawing), the control unit <NUM> ends the series of face recognition processes. On the other hand, if the face recognition fails in step S307 ("NO" in the figure), the process returns to step S301.

In the example of <FIG>, the face image of the person A' in the visible light image is determined as the face image of the person A registered in the storage unit <NUM>, and the face recognition is successful. On the other hand, since the person C is not registered in the storage unit <NUM>, face recognition of the face image of the person C' in the visible light image fails.

<FIG> summarizes a determination result of each step when the face recognition process of the present embodiment is performed based on the images in <FIG>.

In the face recognition process of the present embodiment, face recognition is successful if all the three conditions are satisfied: <NUM>) a person included in a visible light image and a person included in a near-infrared image are the same person, <NUM>) a person in a near-infrared image is an image obtained by capturing a living body, and <NUM>) a person included in a visible light image is a registered person. In <FIG>, face images which satisfy the condition are connected by black bars, and face images which do not satisfy the condition are connected by white bars. The person A satisfies all the above conditions <NUM>) to <NUM>), and thus face recognition is successful. The person B satisfies the above conditions <NUM>) and <NUM>) but does not satisfy the above condition <NUM>), and thus face recognition fails. The person C satisfies the above conditions <NUM>) and <NUM>) but does not satisfy the above condition <NUM>), and thus face recognition fails.

As described above, the face recognition method according to the present embodiment realizes matching between an image captured by a visible light camera and an image captured by a near-infrared camera by using face recognition. Therefore, calibration between the visible light camera and the near-infrared camera is unnecessary. As discussed above, according to the present embodiment, it is possible to accurately determine a living body and a non-living body from a captured image and extract a face image of a living body suitable for face recognition. Thereby, a face recognition system capable of implementing accurate face recognition can be realized.

A computer device according to an embodiment of the present invention will be described with reference to <FIG>. In the present embodiment, an example of a computer device used for implementing the process of each unit in the face recognition system will be described.

<FIG> illustrates an example of the computer device used for implementing the process of each unit in the face recognition system described above. A computer device <NUM> illustrated in <FIG> is not particularly limited but may be of various types or forms. For example, the computer device <NUM> may be a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe, an embedded system, or the like.

The computer device <NUM> includes a processor <NUM>, a memory <NUM>, and a storage device <NUM>. Further, the computer device <NUM> includes a high-speed controller <NUM> including a high-speed interface and a low-speed controller <NUM> including a low-speed interface. The memory <NUM> and a high-speed expansion port <NUM> are connected to the high-speed controller <NUM>. Further, an external input/output device such as a display <NUM> or the like is connected to the high-speed controller <NUM>. On the other hand, a low-speed expansion port <NUM> and the storage device <NUM> are connected to the low-speed controller <NUM>.

The processor <NUM>, the memory <NUM>, the storage device <NUM>, the high-speed controller <NUM>, the low-speed controller <NUM>, and the high-speed expansion port <NUM> are connected to each other through various buses. Further, the processor <NUM>, the memory <NUM>, the storage device <NUM>, the high-speed controller <NUM>, the low-speed controller <NUM>, and the high-speed expansion port <NUM> may be implemented on a common motherboard or may be implemented in other forms as appropriate.

The processor <NUM> is a central processing unit (CPU), for example, and is able to process instructions executed within the computer device <NUM>. Such instructions include an instruction that is used for displaying graphics information of a graphical user interface (GUI) on an external input/output device such as the display <NUM> and stored in the memory <NUM> or the storage device <NUM>.

Further, a plurality of processors, a plurality of busses, or a plurality of processors and a plurality of busses can be used as appropriate together with a plurality of memory devices and multiple types of memory devices. Further, a plurality of computer devices <NUM> can be connected to each device that performs a part of the necessary process. For example, a plurality of computer devices <NUM> can be connected to each other as a server bank, a group of blade servers, or a multiprocessor system.

The memory <NUM> stores therein information within the computer device <NUM>. For example, the memory <NUM> may be a volatile memory unit or a non-volatile memory unit. The memory <NUM> may be another computer readable medium, such as a magnetic disk, an optical disk, or the like, for example.

The storage device <NUM> can configure mass storage used for the computer device <NUM>. The storage device <NUM> may be a computer readable storage medium such as a floppy (registered trademark) disk device, a hard disk device, an optical disk device, a tape device, a solid-state memory device such as a flash memory, a disk array, or the like or include such a computer readable storage medium, for example. The storage device <NUM> may include a storage area network or a device with another configuration. A computer program product may be tangibly embodied in an information carrier. The computer program product can also store an instruction that executes one or a plurality of processes as described above when executed. The information carrier may be a memory device such as the memory <NUM>, the storage device <NUM>, or the memory on the processor <NUM> or may be a computer readable medium or a machine readable medium such as a carrier signal.

The high-speed controller <NUM> manages processes in which the bandwidth for the computer device <NUM> is intensively used. On the other hand, the low-speed controller <NUM> manages processes in which the bandwidth is less intensively used. However, such allocation of the functions is a mere example, and allocation is not limited thereto. Further, a part or a whole of the high-speed controller <NUM> may be incorporated in the processor <NUM>.

The high-speed controller <NUM> is connected to the high-speed expansion port <NUM> that can accept the memory <NUM> and various expansion cards. Further, the high-speed controller <NUM> is connected to the display <NUM> via a graphics processor or an accelerator, for example.

Further, the low-speed controller <NUM> is connected to the storage device <NUM> and the low-speed expansion port <NUM>. The low-speed expansion port <NUM> can include, for example, a communication port of various standards such as Universal Serial Bus (USB), Bluetooth (registered trademark), wired or wireless Ethernet (registered trademark), or the like. One or plurality of input/output devices such as a keyboard, a pointing device, a scanner, or the like can be connected to the low-speed expansion port <NUM>. Further, one or plurality of network devices such as a switch, a router, or the like can be connected to the low-speed expansion port <NUM> via a network adapter, for example.

The computer device <NUM> can be implemented in many different forms. For example, the computer device <NUM> can be implemented in a form of a typical server or a plurality of servers in a form of a group of such servers. Further, the computer device <NUM> can be implemented as a part of the rack server system. Furthermore, the computer device <NUM> can be implemented in a form of a personal computer such as a laptop computer, a desktop computer, or the like.

The computer device <NUM> can function as at least the image processing unit <NUM>, the face recognition unit <NUM>, and the control unit <NUM> of the face recognition system <NUM> described above. The processor <NUM> controls the entire operation of the computer device <NUM> and includes the function of the control unit <NUM>. Further, the processor <NUM> can function as the image processing unit <NUM> by executing the program that implements the function of the image data acquisition unit <NUM>, the face detection unit <NUM>, the liveness check unit <NUM>, the face number determination unit <NUM>, and the face image extraction units <NUM> and <NUM>. Further, the processor <NUM> can function as the face recognition unit <NUM> by executing the program that implements the function of the face feature amount extraction unit <NUM> and the matching unit <NUM>.

That is, the processor <NUM> executes the program that implements the function of each unit of the image data acquisition unit <NUM>, the face detection unit <NUM>, the liveness check unit <NUM>, the face number determination unit <NUM>, the face image extraction units <NUM> and <NUM>, the face feature amount extraction unit <NUM>, and the matching unit <NUM>. Thereby, the processor <NUM> can function as each unit of the image data acquisition unit <NUM>, the face detection unit <NUM>, the liveness check unit <NUM>, the face number determination unit <NUM>, the face image extraction units <NUM> and <NUM>, the face feature amount extraction unit <NUM>, and the matching unit <NUM>. Further, the storage device <NUM> of the computer device <NUM> can function as the storage unit <NUM>.

Note that a part or a whole of the program executed by the processor <NUM> of the computer device <NUM> can be provided by a computer readable storage medium storing the above, such as a digital versatile disc-read only memory (DVD-ROM), a compact disc-read only memory (CD-ROM), a flash memory such as a USB memory or the like.

The face recognition system described above also has a function as an image processing device used for implementing image processing to extract a biometric face image from a visible light image and an image including depth information on an object. The image processing device has the same function as that of the image processing unit <NUM>. That is, according to another embodiment, the image processing device <NUM> can be configured as illustrated in <FIG> is a block diagram illustrating a function configuration of the image processing device according to another embodiment.

As illustrated in <FIG>, the image processing device <NUM> includes an acquisition unit <NUM>, a detection unit <NUM>, a check unit <NUM>, and an extraction unit <NUM>. The acquisition unit <NUM> acquires visible light image data and image data including depth information on an object (for example, near-infrared image data). The detection unit <NUM> detects a face included in an image acquired by the acquisition unit <NUM>. The check unit <NUM> checks whether or not a face in the image detected by the detection unit <NUM> is an image obtained by capturing a living body. The extraction unit <NUM> extracts a face image used for face recognition from a visible light image based on information on a face included in a near-infrared image. The specific function of each unit is the same as the function of each unit of the image processing unit <NUM> described above.

The image processing device <NUM> may further have a function as the same determination unit as the face number determination unit <NUM> included in the image processing unit <NUM> of <FIG>.

Further, the scope of each of the embodiments includes a processing method that stores, in a storage medium, a program causing the configuration of each of the embodiments to operate so as to realize the function of each of the embodiments described above, reads out the program stored in the storage medium as a code, and executes the program in a computer. That is, the scope of each of the embodiments also includes a computer readable storage medium. Further, each of the embodiments includes not only the storage medium in which the program described above is stored but also the program itself.

Claim 1:
An image processing device comprising:
an acquisition unit (<NUM>) that is configured to acquire a first image generated by capturing an object by using a visible light and a camera (<NUM>), a second image generated by capturing the object by using a near-infrared light and a second camera (<NUM>), and depth information on the object by using the second camera (<NUM>) and a projector (<NUM>);
a detection unit (<NUM>) that is configured to detect one or more faces included in the first image and one or more faces included in the second image;
a check unit (<NUM>) that, based on the depth information, is configured to check whether or not a face from the second image is one obtained by capturing a living body;
an extraction unit (<NUM>) that is configured to extract a face image from the first image and a biometric face image from the second image, wherein the biometric face image is a face image that has been detected in the second image and that has been determined to have been obtained by capturing a living body;
a face feature amount extraction unit (<NUM>) that is configured to extract a face feature amount from each of the face image detected from the first image and the biometric face image detected from the second image;
a matching unit (<NUM>) that for all the combinations of the face images detected from the first image and the biometric face images detected from the second image is configured to:
determine, based on comparison between a face feature amount of the biometric face image detected from the second image and a face feature amount of the face image detected from the first image, whether or not a person of the face image detected from the first image is the same as any of persons in the biometric face image detected from the second image;
extract the face image detected from the first image in the case that the face image detected from the first image is the same as any of persons in the biometric face image detected from the second image; and
determine whether or not a person in the face image extracted from the first image is the same as a registered person.