Source: https://patents.google.com/patent/KR101122971B1/en
Timestamp: 2020-02-20 14:37:11
Document Index: 159137385

Matched Legal Cases: ['art 28', 'art 30', 'art 32', 'art 34', 'art 36', 'art 30', 'art 30', 'art 28', 'art 32', 'art 30', 'art 72', 'art 74', 'art 72', 'art 72', 'art 96']

KR101122971B1 - Biometric authentication device, biometric authentication method, and computer-readable recording medium having biometric authentication program recorded thereon - Google Patents
Biometric authentication device, biometric authentication method, and computer-readable recording medium having biometric authentication program recorded thereon Download PDF
KR101122971B1
KR101122971B1 KR1020090113791A KR20090113791A KR101122971B1 KR 101122971 B1 KR101122971 B1 KR 101122971B1 KR 1020090113791 A KR1020090113791 A KR 1020090113791A KR 20090113791 A KR20090113791 A KR 20090113791A KR 101122971 B1 KR101122971 B1 KR 101122971B1
KR1020090113791A
KR20100061353A (en
마사끼 와따나베
2008-11-27 Priority to JP2008303258A priority Critical patent/JP5098973B2/en
2008-11-27 Priority to JPJP-P-2008-303258 priority
2009-11-24 Application filed by 후지쯔 가부시끼가이샤 filed Critical 후지쯔 가부시끼가이샤
2010-06-07 Publication of KR20100061353A publication Critical patent/KR20100061353A/en
2012-03-16 Publication of KR101122971B1 publication Critical patent/KR101122971B1/en
Regarding the determination of the living body, whether or not the living body is used is determined using the movement of the living body. As flexing of a living body, for example, images of a motion from a retracted state such as holding or holding a palm from the open state or an operation from the retracted state to a single state are acquired to acquire a plurality of images, Information indicating the motion of the human body is extracted, and it is determined whether or not it is a living body using the motion information. Further, by comparing the registered motion information (or motion feature information) with the motion information (movement feature information) of the target person, it is determined whether the target person is a registrant.
Palm movement feature information, palm movement common feature, palm sensor, distance map
Biometric authentication device, biometric authentication method, and computer readable recording medium recording a biometric authentication program.
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to biometric determination by an image acquired from a living body such as a palm, and more particularly, a biometric authentication device for determining whether a living body is biometric based on movement information of a living body obtained by stretching of a living body such as opening and closing of a palm. It relates to a method and a biometric authentication program.
In biometric authentication, for example, a palm vessel pattern is used. In such authentication, the reduced hemoglobin contained in the vein near the surface of the palm among the blood vessels utilizes the absorption of near infrared rays. Using a vein pattern extracted as a black line part from the palm image obtained by irradiating near-infrared rays to the palm and photographing the reflected light, identification of a living body and identification of a living individual can be performed.
It is known to determine whether the human body is recognized by measuring the electrical output characteristic of the body and comparing the electrical output characteristic of the body with respect to the biometric determination performed as the processing of the front end of such authentication (Patent Document 1).
Moreover, it is known to analyze the scattered light of the light irradiated to the body by multi-spectrum, and to confirm whether the frequency structure is the same as that emitted from the human body (patent document 2).
Further, in behavioral biometric authentication, it is known to contrast and handle images of human movements and images in two dimensions (Patent Document 3).
[Patent Document 1] Japanese Patent Application Laid-Open No. 2003-111749
[Patent Document 2] Japanese Patent Application Laid-Open No. 2007-524441
[Patent Document 3] Japanese Patent Application Laid-Open No. 2002-150296
By the way, in order to measure the electrical output characteristics (Patent Document 1), it is necessary to contact a part of the living body with the apparatus, and it is not suitable for non-contact palm vein authentication, and it is necessary to arrange the antenna separately from the sensor. There is a problem that a device other than a sensor must be provided. In addition, in the multispectral analysis (patent document 2) of scattered light, an apparatus for confirming the frequency characteristic of light is required, and such apparatus is generally expensive, and there is a problem that the cost of the sensor is increased. In addition, in contrast (patent document 3) which handles the movement of a person and handles the image two-dimensionally, an error occurs because the movement of a person is three-dimensional.
With respect to such a request and a problem, Patent Documents 1, 2, and 3 do not disclose or suggest the matter, and there is no disclosure or suggestion regarding a configuration or the like for solving the problem.
Therefore, the purpose of the biometric authentication device, biometric authentication method, or biometric authentication program of the present disclosure is to determine whether or not a living body is a living body using the movement of a living body with respect to the biometric determination.
Further, another object of the biometric authentication device, biometric authentication method or biometric authentication program of the present disclosure is to determine whether or not a registrant is used for the biometric determination using the movement of a living body.
In order to achieve the above object, the biometric authentication device, the biometric authentication method, or the biometric authentication program of the present disclosure can operate or pinch as a bow of a living body, for example, by grasping or grasping a palm from a flat state. A plurality of images are acquired by capturing the operation from the lean state to the one-side state, information indicating the movement of the living body is extracted from the plurality of images, and the movement information is used to determine whether or not the living body is a living body. Further, by comparing the registered motion information (or motion feature information) with the determiner's motion information (movement feature information), it is determined whether or not the determiner is a registrant and the above object is achieved.
In order to achieve the above object, the biometric authentication device of the present disclosure extracts motion information of a body of a living body from a plurality of images obtained by flexing a living body and images, and the plurality of images obtained by the image capturing means. It is provided with the determination means which judges whether it is recognition.
According to such a structure, it is possible to easily image whether or not the living body is imaged, by extracting the movement information of the living body using a plurality of images obtained from the living body, and using this for the biometric confirmation. In addition, if the motion information is registered, it is possible to determine whether or not theee is a registrant from the registration information and the mover's motion information.
In order to achieve the above object, the biometric authentication method of the present disclosure includes extracting movement information of a living body from a plurality of images obtained by stretching the living body and from the plurality of images obtained by the imaging, and identifying the living body by the movement information. And determining whether or not. Even with such a configuration, the above object is achieved.
In order to achieve the above object, the biometric authentication program of the present disclosure is a biometric authentication program that is executed by a computer and uses an image obtained from a living body, and includes an imaging function for flexing and imaging a living body, and a plurality of images obtained by the imaging. The computer extracts the motion information of the living body from the living body, and executes a judgment function for determining whether the living body is the living body based on the motion information. Even with such a configuration, the above object is achieved.
According to the biometric authentication device, biometric authentication method or biometric authentication program of the present disclosure, the following effects are obtained.
(1) Since the living body is stretched and imaged, and determined using the motion information of the living body from a plurality of images obtained by the imaging, the determination accuracy of whether or not the living body can be improved.
(2) The image is obtained by stretching the living body, and it is determined whether the living body is a living body using the movement information of the living body from the plurality of images obtained by the imaging. The determination precision of whether or not can be raised.
(3) Whether it is a living body is determined by matching with a common model of the movement by the living body's expansion, and therefore, even if there is a slight difference between the movement at the time of registration and the movement at the time of determination, it can be determined whether it is a living body. .
(4) In the non-contact type vascular pattern authentication or the like, determination of whether or not a living body is possible using an image of a living body can be realized by an inexpensive sensor.
(5) Since it is possible to collate using a motion common to humans, and to perform collation by extracting three-dimensional information from a plurality of images, the accuracy of determining whether or not it is a living body is increased.
Further objects, features, and advantages of the present invention will become more apparent by referring to the accompanying drawings and the embodiments.
For the first embodiment, reference is made to FIGS. 1 and 2. 1 is a diagram illustrating a biometric authentication device, and FIG. 2 is a diagram illustrating an example of an operation of a palm sensor. The structure of FIG. 1 and FIG. 2 is an example, Comprising: This invention is not limited to this structure.
This biometric authentication device 2 is an example of an authentication device. In the biometric authentication device 2, as a flexion of a living body such as a hand, a plurality of images are obtained by capturing a transition operation from a state in which the palm is held to a lifted state, i. The motion information of the palm is obtained from the image to determine whether or not it is a human hand. If it is not the movement of the human palm, it is determined that it is not the palm, that is, the living body. The biometric authentication device 2 can be used for biometric authentication using vein authentication or the like, and the measurement of the shape of the authentication target in the biometric authentication device 2 uses a three-dimensional measurement technique based on brightness using active illumination. I use it. In the biometric authentication device 2, the determination of whether the living body is a living body is not an individual movement of the decided person, but a comparison with a movement common to humans, and motion information based on a three-dimensional shape, not similarity of motion images. Similarity with the motion feature extracted from
As shown in FIG. 1, the biometric authentication device 2 uses, for example, the palm 4 as a determination target of a living body, the palm sensor 6 and the palm region extraction unit 8. And palm area distance calculation unit 10, palm motion distance map storage unit 12, palm motion feature extraction unit 14, palm motion common feature extraction unit 16, and palm motion common feature storage unit (18), a palm movement feature collating unit 20, a biometric feature data generating unit 22, a biometric feature storing unit 24, and a biometric feature data collating unit 26 are provided.
The palm sensor 6 is an example of a biometric sensor that acquires an image from a living body, a specific part of a living body, and the like. In this embodiment, the palm sensor 6 detects an image used for determining whether or not a person is recognized from the palm 4 as a determination target. Means. In this palm sensor 6, light is irradiated to the determination object, the reflected light from the palm 4 is captured, the palm 4 is imaged, and the image is acquired. Therefore, this palm sensor 6 is provided with the imaging part 28, the illumination part 30, the illumination control part 32, the imaging control part 34, and the image acquisition part 36. As shown in FIG. The imaging unit 28 is an example of imaging means for imaging the palm 4, and is configured by an imaging device such as a digital camera, and acquires an image for reading a blood vessel pattern of the palm 4. In this embodiment, the image which captures the movement from the state in which the palm 4 is opened to the retracted state, and the image which captures the movement from the state in which the palm 4 is retracted from the state of holding the palm 4 are acquired. The imaging control unit 34 controls the number of times of imaging in accordance with the timing of imaging and the transition of the operation of the palm 4.
The illumination part 30 is a light irradiation means which irradiates light to the palm 4 which is a determination object, and functions to illuminate light uniformly to the palm 4 in order to read the blood vessel pattern of the palm 4 (1st function) ) And a function of emitting beam-shaped light in a known direction different from the optical axis of the imaging unit 28 in order to measure the distance from the palm sensor 6 (the imaging unit 28) to the palm 4 ( 2nd function). The light used for the imaging may be a wavelength sensitive to the imaging unit 28. As shown in FIG. 2, the palm 4 which received the light irradiation from the illumination part 30 reflects light, and the imaging part 28 which captured the reflected light captures the palm 4. As shown in FIG. The illumination control part 32 controls the light of the illumination part 30, and the emitted light quantity.
The image acquisition unit 36 acquires an image obtained by the imaging unit 28, and the image information output from the image acquisition unit 36 is applied to the palm region extraction unit 8.
The palm region extracting section 8 is an example of extracting means for extracting a region of the living body. In this embodiment, the palm region extracting section 8 is an extracting means of the palm region that is the object of the determination information. For example, the palm region extracting unit 8 extracts the palm region by image processing. .
The palm region distance calculating section 10 is an example of calculating means for calculating the distance of the living body region. In this embodiment, the palm region distance calculating section 10 is a means for calculating the distance of any part that can know the position of the palm 4 as the determination target. The distance of the palm area is calculated from the image information. That is, the distance of the image which image | photographed the reference object which has the same uniform reflection coefficient (unknown) in a known distance, and the arbitrary part which can know the position of the palm 4 (decision object) (palm sensor 6). Can be calculated using the second function of the lighting unit 30), and the relative reflection coefficient with respect to the reference object of the palm 4 can be calculated. By using the inverse principle, the palm area is calculated.
The palm movement distance map storage unit 12 is an example of a movement distance map storage means of a living body, and is, for example, a distance map that is composed of data storage means and is generated from a distance calculated by the palm area distance calculation unit 10. Remember.
The palm motion feature extraction unit 14 is a means for extracting the motion feature of the living body. In this embodiment, the palm motion feature extraction unit 14 extracts the motion feature of the palm 4. The movement feature of the palm 4 is extracted from the palm region distance calculated by the palm region distance calculator 10.
The palm motion common feature extractor 16 is a means for extracting a motion common feature of a living body. In this embodiment, the palm motion common feature extractor 16 is a means for extracting a feature common to the motion of the palm 4. The palm motion common feature extraction unit 16 extracts a common feature in the motion of the palm 4 from the distance map representing the motion of the palm 4 stored in the palm motion distance map storage 12.
The palm movement common feature storage unit 18 is an example of storage means for storing the movement common feature of a living body. For example, the palm movement common feature storage unit 18 is composed of data storage means, and the palm movement extracted from the palm movement common feature extraction unit 16 is used. Remember the common features of 4) movement.
The palm movement feature matching unit 20 is an example of matching means for matching the movement feature of the living body. In this embodiment, the palm movement feature matching unit 20 is an example of determination means for judging whether or not the living body is a living body by matching the movement feature of the palm 4. . The palm movement feature collating unit 20 checks the feature of the movement of the palm 4 at the time of registration or authentication and outputs the collation result as a determination result of whether it is a living body.
The biometric characteristic data generation unit 22 uses the palm region information from the palm region extraction unit 8 and the palm movement feature information from the palm movement feature matching unit 20 as means for generating characteristic data of the living body. To generate characteristic data of the living body.
The biometric characteristic storage unit 24 registers personal data for authentication such as vein authentication as storage means for storing the characteristic data of the living body.
The biometric characteristic data collation unit 26 is a collation means for collating the biometric characteristic data during authentication. The biometric characteristic data collation unit 26 registers the registration data in the biometric characteristic storage unit 24 and the biometric characteristic data obtained from the biometric characteristic data generation unit 22. The check is made to determine whether or not the determiner is a registrant by whether the biometric characteristic data of the determination target coincides with the registration data. That is, after the determination of whether the subject is a living body (palm movement feature matching unit 20), authentication of whether or not the registrant is performed is performed.
Next, referring to FIG. 3, FIG. 4, FIG. 5 and FIG. 6 for the determination of whether or not it is a living body. 3 is a flowchart for explaining the comprehensive processing of biometric determination, FIG. 4 is a flowchart for explaining the processing procedure for palm common feature generation, FIG. 5 is a flowchart for explaining the processing procedure for biometric feature registration for determination, 6 is a flowchart for explaining the processing procedure of the determination processing. 3-6 is an example, Comprising: This invention is not limited to this process.
In this biometric determination, the processing procedure (FIG. 3) which makes a determination using the characteristic information in the movement of the palm 4, ie, the transition operation from the flat state to the retracted state, is used as the living body. This processing procedure is an example of a biometric authentication method or a biometric authentication program. As shown in Fig. 3, the palm movement common feature is generated (step S1), and the biometric feature for determination of the judgmentee is registered (step S2). Then, determination processing is performed using the registration information (step S3). Regarding this main routine, as the subroutine, the processing procedure for generating the palm movement common feature (Fig. 4), the processing procedure for determining the biometric feature for determination of the subject (Fig. 5), and the processing procedure for the determination process (Fig. 6) are shown. Is executed.
As shown in Fig. 4, the processing procedure for generating the palm motion common feature is the processing F0 for all the plurality of people, the imaging F1 of the movement of the palms 4 of the plurality of persons, and the images of the captured images. A step of converting to a distance map (F2) is included, and the palm movement common feature is extracted from the distance map of the plurality of persons.
Therefore, in this processing procedure, imaging is started as the processing F0 for all the plurality of people (step S11), and a prompt is made to open the palm 4 (step S12). According to the prompt, the judgmentee straightens the palm 4 and makes it stand out. In this case, the stop of the palm 4 is confirmed (step S13). A prompt is made to pinch the palm 4 from this stationary state (step S14). That is, it captures in response to the state transition from a piece state to a pinch state, and acquires several image. The imaging is terminated (step S15), and as a process for each captured image, the image is converted into a distance map (step S16), and a motion feature is generated (step S17). The average of the movement characteristic of each person is calculated | required from the acquired some image (step S18). The palm motion is stored in the common feature memory 18 (step S19), and the process ends.
The processing procedure of the determination of the biometric feature for determination of the subject includes the processing F3 for checking with the common feature of the movement before the registration. Therefore, in this processing procedure, as shown in Fig. 5, the motion of the palm of the subject to be determined is imaged (step S21), the captured frame is converted into a distance map (step S22), and the palm 4 Is extracted (step S23). After the end of this process, the previously described collation process F3 is executed.
In this collating process F3, the palm movement characteristic of the to-be-determined person and the palm movement common characteristic are compared (step S24), and it is judged whether it agrees (step S25). If they match (YES in step S25), the biometric feature is extracted from the image frame obtained by imaging (step S26), the biometric feature is stored in the biometric feature storage unit 24 (step S27), and the process ends. .
In step S25, if they do not match (NO in step S25), it is determined that registration is impossible (NG) (step S28), and the processing ends.
The processing procedure of the determination processing includes the motion extracted from the image, the matching of the common feature (F4), the matching of the biometric feature and the registered living feature (F5) extracted from the image frame, and based on these matching, It is determined whether or not it is a living body. Therefore, in this processing procedure, as illustrated in FIG. 6, the motion of the palm of the subject to be determined is imaged (step S31), the captured frame is converted into a distance map (step S32), and the palm 4 is displayed. Is extracted (step S33), the palm motion feature of the subjectee is compared with the common feature of palm motion (step S34), and a determination is made as to whether a match is made (step S35), and when it is matched (step S35). For example, the biometric feature is extracted from the captured frame (step S36), the registered biometric information of the subjectee is extracted from the biometric feature storage unit 24 (step S37), and the biometric feature extracted from the captured frame and the living body The biometric features extracted from the feature storage unit 24 are compared (step S38), and a determination is made as to whether or not they are matched (step S39). If they do not match (NO in step S39) Then, it is determined that the judgment is impossible (NG) (step S41), and the process ends.
In addition, in step S35, if there is no agreement (NO in step S35), it is determined as undeterminable (NG) (step S41), and the process ends.
Next, with reference to FIG. 7, FIG. 8, and FIG. 9 regarding extraction of a palm region. FIG. 7 is a view for explaining extraction of a palm area, FIG. 8 is a picture showing an image of a palm in an illuminated state, and FIG. 8 is a picture showing an image of a palm in a non-illuminated state. 7, 8, and 9 are examples, and the present invention is not limited to such a configuration.
In the extraction of the palm region, the intensity of illumination from the palm 4 is adjusted or set, and the distance range within a constant distance, for example, about 10 cm from the illumination unit 30 is determined as the irradiation limit. When the palm 4 is placed on the palm sensor 6 with respect to the setting, the light does not reach the range of. When the palm 4 is placed on the palm sensor 6, the distance from the palm sensor 6 is within the range of about 10 [cm]. do. In this case, the image (FIG. 7 (a), FIG. 8) which image | photographed the palm 4 which received light, and the image (FIG. 7 (b), FIG. 9) which image the palm 4 was turned off light. Get In the image 38 obtained by imaging the palm 4 which received light, the background image 42 appears with the image 40 which shows the palm 4, as shown to Fig.7 (a). In the image 44 obtained by capturing the palm 4 with the light off, as shown in FIG. 7B, both the image 40 and the background image 42 showing the palm 4 appear dark.
Therefore, when the difference between these images 38 and 44 is obtained, the portion other than the image 40 showing the palm 4 from the image 44 (background image) as shown in Fig. 7C. (42)) can be removed, and as a result, a clear image 40 of the palm 4 is obtained. That is, the palm region can be extracted as a non-zero (non-zero) region of the difference image, that is, a region having a luminance value equal to or greater than a predetermined threshold with respect to the luminance of the difference image. FIG. 8 is an actual image of the palm 4 which received light, and FIG. 9 is an actual image of the palm 4 which turned off the light.
In this extraction of the palm region, in more detail, the image obtained by irradiating the palm 4 with light and imaging is set to I on (x, y) {0 <x <w, 0 <y <h}. This is a two-dimensional array in which luminance values are stored in each element by 8 bits without sign. w and h are the width and height of an image, respectively.
The lighting unit 30 is turned off and the image obtained by imaging the palm 4 without light irradiation is set to I off (x, y) {0 <x <w, 0 <y <h}.
Therefore, for each element of these arrays, i.e., the pair of each (i, j) of 0 <i <w, 0 <j <h, the difference between the two images is taken, and the threshold value t is determined by the absolute value of the difference in advance. If it is less than, it is determined that the palm area is not. If this is expressed as a formula,
It is determined that the pixel of (i, j) that satisfies the expression (1) is the palm area, and the pixel that does not satisfy the equation (1) is not the palm area.
Equation 1 may be set to I on (x, y) -I off (x, y) ≠ 0, but by setting the threshold value t, noise included in the image can be removed.
Next, referring to FIGS. 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19 for the calculation of the palm region distance and the distance map. FIG. 10 is a view for explaining the principle of detecting the height, FIG. 11 is a view for obtaining the height, FIG. 12 is a view for explaining the principle of converting the distance using the reflection coefficient, and FIG. 13 is an image of the calibration surface. FIG. 14 is a diagram showing a distance map, FIG. 15 is a diagram showing a palm sensor used for generating a distance map, FIG. 16 is a diagram showing a distance map generation function, and FIG. 17 is a pre-process for generating a distance map. Fig. 18 is a flowchart for explaining the processing procedure of Fig. 18 is a flowchart for explaining the processing procedure for the execution processing for generating the distance map, and Fig. 19 is a photograph showing an example of the palm region distance calculation. 10-19 is an example, Comprising: This invention is not limited to this structure. In FIG. 12, FIG. 15, and FIG. 16, the same code | symbol is attached | subjected to the same part as FIG.
In calculating the distance of the palm region, as shown in FIG. 10, the beam light 46 is irradiated onto the surface of the palm 4 from the light source 31, and the position of the palm 4 which the beam light 46 has touched. Find the height (l) of (48). The light source 31 which emits the beam light 46 includes the lens focal point of the image capturing unit 28 and is located within the plane 52 perpendicular to the optical axis 50 of the image capturing unit 28. 28 is positioned at a distance from the distance d, and the direction of the beam light 46 is in the plane including the imaging section 28, the optical axis 50, and the light source 31. In this case, the optical axis 50 has an inclination of the angle θ with respect to the central axis of the light source 31 parallel to the optical axis 50.
Α is the half angle of view of the imaging unit 28, the height of the palm 4, and the angle at which the plane 52 including the imaging unit 28 and the light source 31 forms the horizontal axis of the image φ (Fig. 11). ), And the plane 52 and the end of the image 64 intersect as shown in FIG. Since the relative relationship on the image 64 at the end of the image 64 at the position of the beam light 46 and the relative relationship on the real world are the same, the following equation holds.
However, W = h / sinφ and a = d + x. The following equation is obtained by solving equation 2 based on the height l of the palm 4.
According to equation (3), the height l of the palm 4 can be obtained from the position 48 at which the beam light 46 on the image 64 touches. The position 48 at which the beam light 46 hits the image 64 is the center of the region where the difference between the image in which the beam light 46 is turned on and the image in which the beam light 46 is turned off is brightly reflected. It is obtained by finding the coordinates of.
Next, when using the measurement result by using a reflection coefficient, see Fig. 12 with respect to the principle of performing the conversion of the distance, the calibration surface image pickup by using a uniform color to the height of the base Z C paper ( 66) It is assumed that there is an image obtained for the brightness of the image. In fact, as the calibration surface 66, the image 68 shown in FIG. 13 is obtained. For the six variables in Fig. 12, since the brightness is reduced in inverse proportion to the square of the distance, the following equation is obtained.
Therefore, when the beam light 46 is used, the luminance E F and the distance Z F at the measurement point on the palm 4 can be known, so that the ratio r F / r C of the reflection coefficient can be obtained. Once r F / r C can be known, E F can be known with respect to a point other than the measurement point by the beam light 46, so that Z F of each point can be obtained. Where E C is the luminance of the calibration surface 66, Z C is the distance of the calibration surface 66, r C is the diffuse reflection coefficient of the calibration surface 66, and E F is of the palm 4 (subject) to be measured. Luminance, Z F is the distance of the palm 4, r F is the diffuse reflection coefficient of the palm 4.
Next, the distance map of the palm 4 is calculated | required. A two-dimensional array in which the distance Z F described above is already stored in each element of the two-dimensional array having the same size as the image of the palm 4 is an array having a distance in the element, but this is called a "distance map". When the distance map is visualized to show the unevenness by the CG technique, as shown in FIG. 14, a visualized distance map is obtained.
As shown in FIG. 15, the palm sensor 6 having a distance map generation function is provided with an image capturing unit 28 in the center of the light shielding frame 70, and as a plurality of lighting units 30 around the same. The palm lighting apparatuses 302, 304, 306, and 308 and the beam light illuminating apparatus 310 (light source 31) are provided. The palm lighting apparatuses 302, 304, 306, and 308 are light sources that irradiate the entire palm, and the beam light lighting apparatus 310 is a light source that irradiates the beam light 46.
When such a palm sensor 6 is used, as a structure for realizing a distance map generation function, as shown in FIG. 16, the calibration surface storage part 72 and the beam light position detection part 74 are provided. The calibration surface storage unit 72 is constituted by a storage medium, and stores the height, the image, and the like of the calibration surface 66 as already described information about the calibration surface 66. The beam light position detection unit 74 detects the position where the beam light 46 has reached. In this case, the palm area distance calculation unit 10 calculates the distance of the palm area based on the information about the calibration surface 66, the palm area, and the beam light position, and generates a distance map. It consists.
This distance map generation performs execution processing (FIG. 18) after a prior process (FIG. 17). The processing procedure of the pre-processing is the above-described processing using the calibration surface 66. As shown in FIG. 17, an object of a uniform color (calibration surface 66) is arranged at a known height (step S51). The calibration surface 66 is imaged (step S52), the height and the image of the calibration surface 66 are stored in the calibration surface storage unit 72 (step S53), and the preprocessing ends.
In addition, in the processing procedure of the execution process, as shown in FIG. 18, the beam light illuminating device 310 is turned on (step S61), and the palm 4 is imaged using the beam light 46 (step). S62), the image obtained by this imaging is stored in the calibration surface storage part 72 as an image B (step S63).
The beam light illuminating device 310 is turned off (step S64), and the palm lighting devices 302, 304, 306, and 308 are turned on to illuminate the palm 4 (step S65). By the light irradiation of the whole palm, the palm 4 is imaged (step S66), and the image obtained by this imaging is stored in the calibration surface storage part 72 as image A (step S67).
The palm lighting apparatuses 302, 304, 306, 308 are turned off (step S68), imaging of the palm 4 without light irradiation is performed (step S69), and the image obtained by this imaging is stored as the image C ( Step S70).
The difference between image B and image C is taken, and this difference is referred to as image D (step S71). The center coordinates of the bright area of the image D are obtained (step S72), the ratio r F / r C of the reflection coefficient between the palm 4 and the calibration surface 66 is calculated (step S73), and the image A and the image C The palm region S is obtained by taking the difference (step S74). The distance from the palm sensor 6 is calculated for each pixel of the palm region S (step S75), and distance map data is obtained. As a result, this execution processing ends.
In this process, as shown in FIG. 19, the imaging unit 28 obtains an image 76 of the palm 4 by imaging. In this case, since the portion enclosed by the frame 78 is in the state of being lined up toward the hand bottom sensor 6 side, it appears larger than the other fingers on the image 76, whereby the state of being lined up can be seen.
Next, referring to FIG. 20 and FIG. 21 for extraction of palm movement. 20 is a flowchart for explaining a processing procedure of palm movement extraction, and FIG. 21 is a flowchart for explaining a processing procedure of normalization processing of information representation of palm movement. 20 and 21 are examples, and the present invention is not limited to these processes.
In the motion extraction of the palm, as the acquisition of an image showing the transition of the motion from the open state to the retracted state of the palm 4, as shown in FIG. 20, the target frame is extracted (step S81). The normalization process of the information expression of the motion of (4) is performed (step S82).
In extraction of the target frame (step S81), the target person is prompted to open the palm 4, the image is monitored and monitored, and the palm area distance is calculated for each frame (Fig. 18). It is determined that the palm 4 is unfolded (unfolded) when the dispersion of the value of the distance in the palm area is equal to or less than a predetermined threshold value and the variation is eliminated in successive frames. Calculation of the distance between the monitor of this image and the palm area is continued.
From this state, the judgmentee is prompted to pinch the palm 4. Similarly, the variance of the distance value in the palm area is obtained, and at the time when there is no change in successive frames, it is judged that the movement of the palm 4 has stopped (retracted), and the palm of the distance is obtained. We consider to be image of distance when we cut.
The image frame from when it is determined that the palm 4 is closed and until it is determined that the palm 4 is closed is the palm (from when the palm 4 is closed until the palm 4 is closed. It can be regarded that the movement of 4) is recorded by the distance value.
Next, in the information expression (step S82) of the movement of the palm, the processing procedure of the normalization process (FIG. 21) is executed. In this processing procedure, as shown in FIG. 21, the distance value is normalized (step S91), and the minimum and maximum values in the palm area are obtained for all image frames. Normalization is performed on all the frames so that the difference is the largest frame and the difference is the predetermined K.
Next, the height of the palm area is normalized (step S92). Find the maximum value of the distance in the palm area. The distance image of the palm 4 is changed so that the maximum value may be seen when it is moved to a predetermined height H. In other words, when the maximum value is the height h, the palm area is enlarged or scaled at the magnification of H / h. And the value of a pixel adds H-h.
Next, normalization of the size is performed so that the area of the palm region of each frame becomes a predetermined S pixel (step S93), the position of the palm region is normalized (step S94), and the direction of the palm region is normalized (step S94). Step S95). In this direction normalization, the two-dimensional moment of the palm 4 is calculated, and two-dimensional rotation is performed so that the long axis is along the longitudinal direction of the image.
Then, time normalization of the motion frame of the palm 4 is performed (step S96). That is, the time of the frame when the palm 4 is opened is t = 0 and the time of the frame when the palm is closed is t = 1, and the image frame is in the middle of the time t = 0-1. Temporal normalization is performed. Finally, time t is recorded from t = 0 to t = 1, for example, 11 frames at a time of 0.1, and when there are no frames from time t = 0.1 to time t = 0.9, the closest Interpolate the image of the frame.
(1) Normalization of the value of the distance (step S91)
See FIG. 22 and FIG. 23 for normalization of the value of this distance. FIG. 22A is a view showing a palm open state, FIG. 22B is a view showing a palm closed state, and FIG. 23 is a view showing an image frame. 22 and 23 are examples, and the present invention is not limited to such a configuration.
Normalization of the value of the distance is performed using the image frame (FIG. 23) of the palm 4 which reaches from the open state (FIG. 22 (a)) to the retracted state. The minimum value d min and the maximum value d max in the palm area are obtained. As shown in Fig. 22B, in the state in which the palm 4 is just closed, the thumb hangs up and approaches the palm sensor 6 most, so that the minimum value d min is reached. Therefore, normalization is performed for all frames so that the difference between the minimum value d min and the maximum value d max (d max -d min ) is the largest frame and the difference becomes a predetermined K (Fig. 22 (b)). Do it. As shown in FIG. 23, when the image frame 80801 is when the palm 4 is closed from the image frame 801 when the palm 4 is extended, it is n image frames. When the time t is taken along the horizontal axis, the image frames 801, 802, ..., 80n are transferred.
Thus, for each image frame (801 ~ 80n), it calculates the maximum value d max, the minimum value d min and, with respect to the the difference (d max -d min) is big image frame (80k), difference (d max - d min ) is converted to K.
Difference (d max -d min) is when the number of the largest frame in the k max, the distance map of the frame k d k (i, j) the distance map d 'k (i, j) after the normalization, the following formula: Obtained by
However, d max and d min of the kth frame are set to d max (k) and d min (k), respectively.
(2) Normalization of the height of the palm area (step S92)
The maximum value of the distance in the palm area is obtained, and the distance image of the palm 4 is converted so that the maximum value is visible when the maximum value is moved to a height of a predetermined height H. That is, when the maximum value is h, the palm area is enlarged or scaled at the magnification of H / h. And the value of a pixel adds H-h. A schematic diagram of the palm 4 in an open state is shown in FIG. 24.
The distance map d '' (i, j) after normalization of the distance map d '(i, j) of each frame is obtained by the following equation.
(3) Normalization of the size of the palm area (step S93)
Next, the size is normalized so that the area of the palm area of each frame becomes a predetermined S pixel. FIG. 25A shows the frame 81 of the area s before normalization, and FIG. 25B shows the frame 82 of the area S after normalization. By this process, the frame 81 (Fig. 25 (a)) of the area s is normalized to the frame 82 (Fig. 25 (b)) of the area S.
If the area of the palm area of each frame is s, the distance map d '' '(i, j) after normalization of the distance map d' '(i, j) of each frame is obtained by the following equation.
(4) Normalization of the position of the palm area (step S94)
The position is normalized so that the center of gravity G of the palm region is at the center of the image so that the position of the palm 4 of each frame is at the center. In the normalization of this position, as shown in Fig. 26A, when the center of gravity coordinates of the palm region of each frame is (C x , C y ), as shown in Fig. 26B, Normalization is performed to shift the center of gravity coordinates (C x , C y ) to the center of the image. If the center coordinate of the image is (0, 0), the distance map d '''' (i, j) after normalization of the distance map d '''(i, j) of each frame is obtained by the following equation. Lose. Each frame 83 is converted into each normalized frame 84, as shown in Fig. 26B.
(5) Normalization of the direction of the palm area (step S95)
A two-dimensional moment is calculated and two-dimensional rotation is performed so that the long axis is along the longitudinal direction of the image. The inertia major axis is used for normalization in this direction. The long axis 86 (FIG. 27 (a)) of the palm region can be said to be an inertia major axis. Therefore, as shown in Fig. 27A, the coordinates (x-axis, y-axis) are taken on the image 88, the center O of the image 88 is the origin, and the inertial main axis 86 is the y-axis. In the case of the angle θ with respect to, the distance map d '' '' '(i', j ') after normalization of the distance map d' '' '(i, j) of each frame 91 is obtained by the following equation. Lose. FIG. 27B is a normalized frame 92.
As a method of obtaining such an inertia spindle 86, for example, Yachida Masahiko's Robot Vision pp.91-94 or the like.
(6) Normalization of time of palm movement frame (step S96)
As shown in FIG. 28, the time of the image frame 811 when the palm 4 is removed is made t = 0, and the time of the image frame 81n when the palm 4 is closed is t = 1. Normally, the intermediate image frame is a frame between the times t = 0 and 1, and is normalized in time. Finally, 11 frames are recorded, for example, at time t from 0 to 1, in increments of 0.1. When no frame exists from time t = 0.1 to time t = 0.9, it is obtained by interpolating the image of the nearest frame.
Then, the distance map d '' '' 'k' (i, j) of the k 'frame after normalization (0≤k'≤11, k' is an integer) is the distance map d '' '' 'before normalization. From k (i, j) (s ≦ k ≦ e, k is an integer and s, e are as described in FIG. 28), and are obtained by the following equation. first,
For k, where k is an integer,
If k is not an integer, [] is a Gaussian symbol,
Next, with respect to the extraction of the common feature of the palm motion, the palm motion already described is collected for a plurality of persons, and the distance map of each frame is averaged in units of pixels. As a result, the already described eleven frames (FIG. 28) are taken as common features of the movement of the palm 4. The common features of the obtained movements of the palm 4 are stored in the palm movement common feature storage unit 18.
Next, in the verification of the palm motion feature, the biometric information is registered for palm vein authentication, and in the verification, the palm motion information is checked in order to determine whether or not the palm (object) placed on the palm is a living body. According to the process already described, the feature of the motion of the palm of the subject to be determined is acquired, and then the common feature of the motion of the palm 4 stored in the palm motion common feature memory 18 is obtained, and both are distances. Since it is normalized by the value, the area, the direction, and the time, the comparison can be performed by comparing the images (11 (a) and 29 (b)) of 11 frames. FIG. 29A is a diagram showing 11 frames showing a palm movement common feature, and FIG. 29B is a diagram showing 11 frames showing a palm movement feature of the subject.
The comparison of these images finds and accumulates the difference of distance values for each pixel over 11 frames. Then, if the cumulative value is equal to or less than the predetermined threshold value, it can be determined that the determination target performed a movement similar to the living body.
As shown in Fig. 30A, the k-th frame 84k showing the common features and the k-th frame showing the features of the judgmentee as shown in Fig. 30B ( 85k), if the common feature d mk (i, j) of the frame 84k and the feature d nk (i, j) of the determiner of the frame 85k are used, the correspondence of each frame 84k, 85k Find the sum of the differences between the coordinates.
When the palm region of the common characteristic frame (84k) of a R k, the accumulated value M of the differences can be determined from the equation below.
When the accumulated value M is a predetermined threshold value M th or less can be determined that the line (M≤M th), determining the movement destination is similar to the living body, and, consistent with the properties, it is confirmed that the subscriber.
The advantages and features of the first embodiment described above are listed as follows.
(1) As a living body, for example, it is possible to confirm by using the motion information of the palm from a plurality of images acquired by imaging of the palm. In this confirmation, it is possible to determine whether it is a living body, and furthermore, registered motion information. By contrast, you can check whether the subject is a registered person.
(2) Since the palm is used as the determination site of the living body and the movement information is acquired by using the opening and closing of the palm as an example of the expansion of the living body, the judged person does not force a special operation only by the simple operation of opening and closing of the palm, There is an advantage that it can be determined whether or not it is a living body.
(3) In the non-contact palm vein pattern authentication, the palm biometric method can be realized by a low cost sensor.
(4) It is possible to collate using the movement of a living body common to humans, and to perform the collation by extracting three-dimensional information from a plurality of images, thereby improving the determination accuracy.
(5) The biometric determination is performed based on the image picked up by irradiating the palm with light, and the individual can be confirmed based on the determination.
(6) Since the biometric authentication device has an irradiation component and an imaging component, and performs registration, collation, or identification for biometric determination, the biometric authentication device instructs the opening and closing of the hand and performs biometric confirmation with or without the movement. Whether or not it is a living body can be easily confirmed.
(7) As a living body, for example, confirmation of the movement of the palm can be determined by matching the movement of the palm of the subject to the palm and the movement information of the palm collected from a plurality of persons, thereby performing one-to-one authentication. .
(8) As a living body, for example, in the measurement of the movement of the palm, an image having a value having the value of the irradiation position of the beam light and the distance to each point of the palm calculated from the intensity of the reflected light as a value is used, so that high precision determination can be performed. Can be. The intensity of the reflected light is the intensity of the reflected light incident on the imaging unit 28 from the palm 4 and is the luminance of each pixel of the image.
(9) As a living body, for example, an image having a distance for determining the movement of a palm as a value uses a plurality of images captured from the time when the palm is instructed to open and the palm is instructed, Motion information is acquired from such an image.
(10) As a living body, for example, the motion information of a palm is normalized with respect to the motion of the palm, the height of the palm, the size of the palm, the direction of the palm, and the time when the image is taken.
(11) As a living body, for example, the motion information of the palm is normalized with respect to the time when the numerical value indicating the irregularities of the image normalized with respect to the palm motion from the plurality of images is taken as a value. do.
(12) When judging whether the living body is a living body, the palm is held to check whether or not deformation is performed in the same way as the human hand. Determine that no. Since the three-dimensional measurement technique based on the brightness | luminance using active illumination is used for the measurement of the shape of an object, determination precision can be improved.
(13) As a living body, for example, a counterfeit by an object having a reflection characteristic equivalent to that of a palm can be determined as not a hand.
Next, with reference to FIG. 31, FIG. 32, and FIG. 33 about 2nd Embodiment. FIG. 31 is a flowchart for explaining a processing procedure according to the second embodiment, FIG. 32 shows an ellipsoidal sphere to be applied to a normalized palm image, and FIG. 33 shows an application of the ellipsoidal sphere in opening and closing of the palm. 31, 32, and 33 are examples, and the present invention is not limited to such a configuration.
In the biometric authentication device, biometric authentication program or biometric authentication method according to the second embodiment, although the information expression of the movement of the palm is different from the first embodiment, the same device as that of the first embodiment (Fig. 1) is used.
In this processing procedure, as shown in FIG. 31, the normalization of the value of the distance (step S101), the normalization of the height of the palm region (step S102), and the normalization of the size of the palm region (step S103) are performed in the first embodiment ( 21 and the description thereof will be omitted.
In this embodiment, after normalizing the size of the palm region, an ellipsoid sphere is assumed as an example of a geometric model and applied to this ellipsoid sphere (step S104). That is, the ellipsoid 92 (FIGS. 32 and 33) is applied to the image generated through the steps S101 to S103, where the value of the distance, the height of the palm region, and the size of the palm region are normalized. Based on the application of the elliptic sphere 92, the normalization of the time of the motion frame of the coefficient of the ellipsoid sphere 92 is normalized (step S105), and the process is terminated.
As shown in FIG. 32, the coordinate system at the time of applying the ellipsoid 92 to the palm region takes the center coordinates (X, Y, Z) in the ellipsoid 92 and the imaging unit 28 of the palm sensor 6. Based on the origin Q of). In this case, a is the distance to the farthest portion from the x-axis direction, b is the Y-axis direction, c is the Z-axis direction, the coordinates (X 1, Y 1, Z 1) of tawongu 92.
Therefore, if the upper left corner of the image is the origin, the following equations representing these coordinate systems and the ellipsoid 92 are set.
Therefore, the coefficients of X 1 , Y 1 , Z 1 , a, b, and c in this equation are obtained. As a method of obtaining this coefficient, an iterative approximation method using the least square method, the Newton method, the Marquad method, or the like may be used, with the pixel in the area of the palm 4 as the sample point (sample) on the distance map. As a result, six coefficients (X 1 , Y 1 , Z 1 , a, b, c) are obtained as a result of applying the ellipsoid sphere 92 to each frame.
The application of the ellipsoidal sphere 92, which is an example of the previously described geometrical model for the palm region, shows that the palm 4 is unfolded when the operation is shifted from the open state to the closed state. As shown in a), the concave portion of the palm 4 is in contact with the ellipsoid 92, and from this state, as shown in FIG. 33B, the ellipsoid ( 92), the ellipsoid 92 is enclosed with each finger, and as shown in Fig. 33 (c), the finger is moved in the direction in which the palm 4 is pinched. . In this case, the ellipse sphere 92 applied to the palm 4 becomes thin and long. In the state immediately before the palm 4 is retracted, as shown in FIG. 33D, the palm 4 in a curved state is nested inside the ellipsoid 92. In a state in which the palm 4 is completely closed and the fist is formed, as shown in FIG. 33E, the spherical surface of the elliptic sphere 92 coincides with the lower surface side of the palm 4. Thus, the operation | movement from the state which opened the palm 4 to the state which squeezed the palm 4 corresponded to the outline of the ellipse sphere 92 is shown in (a) of FIG. 33-(b)-FIG. 33 (c)-FIG. Transition is performed as shown in (d) of FIG. 33 → (e) of FIG. This information is used to determine whether the operation transition is a living body.
Therefore, the time of the frame when the palm 4 is removed is t = 0 and the time of the frame when the palm 4 is closed is t = 1, and the halfway frame is the time t = 0 to 1 frame. Normalized in time. Finally, six coefficients corresponding to 11 frames at 0.1 steps are recorded for the time t from t = 0 to 1. When no frame exists from time t = 0.1 to time t = 0.9, the coefficients of the elliptical sphere 92 of the image of the nearest frame are obtained by interpolating for each coefficient. In order to obtain the common feature of the movement of the palm 4, the average of the information of the plurality of persons may be calculated for each frame and for each coefficient of the elliptic sphere 92.
In addition, when a comparison between the motion of the determination target and the common feature of the motion is obtained, the difference is calculated for each frame and for each coefficient of the elliptic sphere, and the sum of the weighting of the differences is obtained according to a predetermined weight, It is possible to determine whether or not the accumulated value is equal to the movement of the living body by whether or not it is lower than a predetermined threshold value.
In this embodiment, the feature matters are enumerated as follows.
(1) The motion information of a palm image | photographs the coefficient which prescribes the geometric model at the time of applying the geometric model to the image which has distance as a value and normalized about the motion of a palm, the height of a palm, and the size of a palm. It is normalized with respect to time.
(2) Whether it is a living body is determined by matching with a common model of the movement of the palm, so whether or not it is a living body can be determined even if the movement of the palm at the time of registration and the movement of the palm at the time of determination are somewhat different. After confirming that, is a living body, it can be confirmed whether or not it is a specific individual.
Next, with reference to FIG. 34 and FIG. 35 about 3rd Embodiment. 34 is a flowchart for explaining a processing procedure according to the third embodiment, and FIG. 35 is a view for explaining the calculation of the unevenness rate of the palm. 34 and 35 are examples, and the present invention is not limited to such a configuration.
In this 3rd Embodiment, the process of calculating the uneven | corrugated rate of the palm 4 is included in the information expression of the movement of a palm, and it differs from 1st Embodiment in this point. Therefore, in the processing procedure of the third embodiment, as shown in FIG. 34, the value of the distance is normalized as in the first embodiment (FIG. 21) (step S111), and after the normalization, the value of the distance is determined. With respect to the image, the unevenness rate indicating the ratio in the palm area of the pixel having a distance deviating from the reference is calculated based on the height of the center of the palm area (step S112), and the time normalization of the motion frame of this uneven rate is normalized. (Step S113).
In calculation of this uneven | corrugated rate (step S112), suppose the circumference rectangle 94 as an example of a geometry model to a palm area | region, and apply this circumference rectangle 94 (FIG. 35 (a)), and circumference rectangle 94 The value h of the height of the pixel of the center P of () is obtained. The number of pixels s of h ± Δh according to the predetermined Δh in the palm area is obtained. That is, the number of pixels s is the number of pixels deviating by Δh from the center P of the circumscribed rectangle 94 of the palm region. Since the pixel number s is an area, if the entire area of the palm area is S, the ratio of the area s to S is r.
This is obtained as an uneven rate.
As shown in Fig. 35A, in the distance map d 0 (i, j) of the 0th frame when the palm 4 is open, when the palm area is R 0 , the circumscribed rectangle 94 Is a line of contact with the outline of the finger furthest from the center of the open palm 4.
When the palm 4 starts to be pinched, as shown in FIG. 35B, the circumscribed rectangle 94 is reduced in accordance with the change in the appearance of the palm 4. The part 96 shown with an oblique line in a figure is the part of the pixel which deviated by (DELTA) h from the center P of the circumscribed rectangle 94 of a palm area | region. At the final position in which the palm 4 is pinched and pinched, as shown in FIG. 35C, the circumscribed quadrangle 94 is further reduced in accordance with the change in the appearance of the palm 4, and the palm area. The portion of the pixel deviating by Δh from the center P of the circumscribed quadrangle 94 of is expanded, and the portion of the pixel deviating by Δh to the vicinity of the wrist is spread. When the pixels deviating from the height of the center of the circumferential rectangle 94 of the palm region by Δh are distributed as shown by the diagonal lines, and the palm 4 is shifted from the open state to the pinched state, the operation transition changes in the unevenness rate. It can be seen by.
If the coordinate of the center P of the circumscribed rectangle 94 is (Cx, Cy), the height h of the coordinate is obtained as d 0 (Cx, Cy). A distance map d 0 (i, j) the palm region R 0 because the pixel number s has a value of h ± Δh in accordance with a predetermined Δh obtained in, of the whole of the palm region R 0 area S (the palm region R 0 on the The ratio r of the number of pixels s to the number of pixels included) is calculated as described above.
In the normalization of the time of the movement frame of the unevenness rate (step S113), as described above, the time of the frame when the palm 4 is opened is t = 0 and the time of the frame when the palm 4 is pinched. Let t be t = 1, and make the middle frame a frame between time t = 0 and 1, and normalize it temporally. Finally, the unevenness ratio corresponding to 11 frames of 0.1 sheets at each time t from 0 to 1 is recorded. If there is no frame from time 0.1 to time 0.9, it is obtained by interpolating the unevenness rate of the image of the nearest frame.
In order to obtain a common feature of the movement, in order to average the information of a plurality of persons, the average of the unevenness rate is obtained for each frame. In addition, when determining the contrast between the movement of the determination target and the common feature of the movement, the difference between the unevenness rate is determined for each frame, and whether or not the value accumulated over all the frames is lower than a predetermined threshold value, What is necessary is just to determine whether it is the same as the movement of a living body.
According to this embodiment, the motion information of the palm has a distance as a value, and the motion information with a high precision can be obtained from a plurality of images as a maximum value indicating the unevenness of the image normalized to the palm motion. By using such motion information, it is possible to increase the determination accuracy of the biometric determination.
Next, with reference to FIG. 36, 4th Embodiment is referred. 36 is a flowchart for explaining a processing procedure according to the fourth embodiment. The structure shown in FIG. 36 is an example, Comprising: This invention is not limited to this structure.
In this fourth embodiment, the processing procedure (Fig. 34) according to the third embodiment is simplified.
In this processing procedure, as shown in FIG. 36, normalization of the value of the distance (step S121) and calculation of the unevenness rate (step S122) are performed to calculate the maximum unevenness rate (step S123). The processing of normalizing the value of the distance (step S121) and calculating the unevenness rate (step S122) is the same as in the third embodiment.
In calculation of the maximum unevenness rate (step S123), the maximum value is calculated from the unevenness rate from the frame when the palm 4 is opened to the frame when the palm 4 is closed. This maximum unevenness ratio is used for the movement feature. Therefore, in order to obtain the average of the information of a plurality of persons in order to obtain a common feature of movement, the average of the maximum unevenness ratio is calculated. In this case, the maximum unevenness ratio calculates the unevenness ratio r already described from the 0th frame to the 10th frame, and among them, the maximum value of r is the maximum unevenness ratio.
When determining the comparison between the motion of the judgment object and the common feature of the motion, it is judged by whether or not the difference between the maximum unevenness ratio is lower than a predetermined threshold value. In other words, it is checked whether the object of determination changes to be equal to the movement of the living body.
For the fifth embodiment, reference is made to FIG. 37. 37 is a diagram illustrating a hardware configuration of a biometric authentication device according to a fifth embodiment. The configuration shown in FIG. 37 is an example, and the present invention is not limited to such a configuration. In Fig. 37, the same parts as those in Fig. 1 are denoted by the same reference numerals.
As shown in FIG. 37, the biometric authentication device 2 includes a processor 100, a program storage unit 102, a data storage unit 104, a random access memory (RAM) 106, And a display unit 108 and a palm sensor 6, and these functional units are linked by a bus 110.
The processor 100 executes an operating system (OS) 112 in the program storage unit 102, and executes various application programs in addition to the biometric authentication program 114.
The program storage unit 102 is composed of a recording medium, and stores various applications, subroutines, and the like, in addition to the OS 112 and the biometric authentication program 114 described above. The data storage unit 104 is composed of a recording medium, and the previously described palm movement distance map storage unit 12, palm movement common feature storage unit 18, biometric feature storage unit 24, and calibration surface storage unit 72 are described. Equipped with. RAM 106 constitutes a work area.
The display unit 108 is constituted by an LCD (Liquid Crystal Display) display or the like as an example of the information presentation means, and displays an indication message during a process such as determination, a determination result, or the like. The instruction message during the process is an instruction of opening and closing of the palm 4 to the target person. This instruction message may be performed by voice, and the display unit 108 may include voice generating means for issuing a voice message.
As described above, the palm sensor 6 includes an imaging unit 28, an illumination unit 30, an illumination control unit 32, an imaging control unit 34, and an image acquisition unit 36. The unit is controlled by the processor 100.
According to this configuration, based on the execution of the OS 112 and the biometric authentication program 114, the previously described palm region extraction unit 8, palm region distance calculating unit 10, palm movement feature extraction unit 14, The palm motion common feature extractor 16, the palm motion feature checker 20, the biometric feature data generator 22, and the biometric feature data checker 26 include a processor 100 and a RAM 106. The processing already described is executed.
(1) Although the opening and closing of the palm were illustrated as an example of the flexion of the living body, the living body may be the flexing of living parts such as hands, arms, and feet in addition to the palm. In this case, the palm sensor 6 is a biometric sensor, the palm region extractor 8 is a biometric region extractor, the palm region distance calculator 10 is a bioregion distance calculator, and the palm motion distance map storage 12 is The movement distance map memory of the living body, the palm motion feature extraction unit 14 is a movement feature extraction unit of the living body, the palm motion common feature extraction unit 16 is the movement common feature extraction unit, the palm motion common feature storage unit 18 ), The living body common feature storage unit, the palm motion feature matching unit 20, the living body feature matching unit, the living body feature data generation unit 22, the living body feature storage unit 24, the living body feature data collation unit 26 ) May be configured.
(2) Imaging of hand flexion, palm flexion or finger flexion with respect to opening and closing of the palm of the above embodiment, and using a plurality of images indicating the flexion of the hand, palm or finger as in the above embodiment, It may be configured to determine whether or not. When the movement of the hand as the living body is the determination target, it is possible to determine whether the living body is simple and with high precision, and as a result, the accuracy of the biometric authentication can be improved.
(3) In the above-described embodiment, the transition operation from the open state of the palm to the retracted state is imaged. However, the transition from the retracted state to the detached state is made using the retracted state of the palm 4 as the operation start point. You may make it the structure which image-images.
(4) In the above embodiment, the geometric model is assumed as a means for specifying the movement of the palm, and the elliptical sphere 92 (FIG. 32) and the circumscribed rectangle 94 (FIG. 35) are illustrated as an example. Other polyhedrons or spheres may be applied to the motion of to calculate the coefficients already described for specifying the unevenness or the distance from the reference position.
(5) Although the palm image has been exemplified as an example of vein authentication in the above embodiment, the present invention can also be applied to determining whether or not it is a living body using motion information of the palm other than vein authentication.
(6) In the above embodiment, the palm sensor 6 is illustrated as a means for acquiring a palm image. However, in the present invention, other detection means for acquiring judgment information may be used as a sensor for acquiring an image other than the palm. .
As described above, the most preferred embodiments of the biometric authentication device, the biometric authentication method, the biometric authentication program and the like have been described, but the present invention is not limited to the above description, but is described in the claims or implements the invention. Based on the gist of the invention disclosed in the best mode for carrying out, various modifications and changes are possible to those skilled in the art, and of course, such modifications and changes are included in the scope of the present invention.
The biometric authentication device, the biometric authentication method, and the biometric authentication program of the present disclosure capture images by transitioning a palm from a pinched state or a pinched state to a pinched state, for example, as a living body. Then, it is possible to determine whether or not the living body is a living body using the movement information of the living body from the plurality of images acquired by the imaging, and furthermore, whether or not the subject is the registered person by contrast with the registered movement information. Whether or not it can be determined can be widely used for vein authentication, form authentication, and the like, which is useful.
1 is a diagram showing a biometric authentication device according to a first embodiment.
2 is a diagram illustrating an example of the operation of a palm sensor;
3 is a flowchart for describing comprehensive processing of biometric determination.
4 is a flowchart for explaining a processing procedure for generating palm motion common features.
Fig. 5 is a flowchart for explaining a processing procedure of biometric feature registration for determination of a subject.
6 is a flowchart for explaining a processing procedure of a determination process.
7 is a diagram for explaining extraction of a palm region.
8 is a photograph showing an image showing a palm in an illuminated state.
9 is a photograph showing an image showing a palm in the absence of illumination.
10 is a diagram for explaining the principle of height detection.
11 is a diagram for obtaining a height.
12 is a diagram for explaining the principle of converting a distance using a reflection coefficient;
Fig. 13 is a diagram showing an image of a calibration surface.
14 shows a distance map.
15 is a diagram illustrating a palm sensor used for generating a distance map.
16 is a diagram illustrating a distance map generation function.
Fig. 17 is a flowchart for explaining a processing procedure of preprocessing for generating a distance map.
18 is a flowchart for explaining a processing procedure of execution processing for generating a distance map.
19 is a photograph showing an example of calculating a palm region distance.
20 is a flowchart for explaining a processing procedure of palm motion extraction.
Fig. 21 is a flowchart for explaining the processing procedure of the normalization processing of the information expression of the movement of the palm.
Fig. 22 is a diagram showing a palm open state and a pinched state;
23 illustrates an image frame.
24 shows normalization of the height of the palm region.
25 shows normalization of the size of the palm region.
Fig. 26 shows normalization of the position of the palm region;
27 illustrates normalization of the direction of the palm region.
Fig. 28 is a diagram illustrating normalization of time of a palm movement frame.
Fig. 29 shows the palm movement common features and the movement features of the subject.
30 is a diagram illustrating a comparison of image frames.
31 is a flowchart for explaining a processing procedure according to the second embodiment.
Fig. 32 shows an ellipsoidal sphere to be applied to a normalized palm image.
FIG. 33 shows the application of an ellipsoid to opening and closing of a palm. FIG.
34 is a flowchart for explaining a processing procedure according to the third embodiment.
35 is a diagram for explaining calculation of the uneven rate of the palm.
36 is a flowchart for explaining a processing procedure according to the fourth embodiment.
37 is a diagram showing the hardware configuration of a biometric authentication device according to a fifth embodiment.
2: biometric authentication device
6: palm sensor
8: palm region extracting unit
10: palm area distance calculation unit
12: palm movement distance map memory
14: palm movement feature extraction unit
16: palm feature common feature extraction unit
18: palm movement common feature memory
20: palm movement feature contrast
22: biometric data generation unit
24: biological feature storage unit
26: biometric data control unit
30: lighting unit
32: lighting control unit
34: imaging control unit
36: image acquisition unit
38, 40: burns
42: Background image
44: burns
46: beam light
48: location
50: optical axis
52: flat
66: calibration surface
68: the burn
70 measuring point
72: calibration surface storage unit
74: beam light position detection unit
76: the image
78: frame
801 to 80n: picture frame
81, 82, 83, 84, 91, 92: frames
86: long axis (inertia spindle)
88: burns
90: frame
92 oval sphere
94: circumscribed rectangle
102: program storage unit
104: data storage unit
110: the bus
112: OS
114: biometric authentication program
Imaging means for flexing and imaging a part of a living body that can obtain biological information;
Determination means for extracting the motion information of the stretching of the living body from the plurality of images obtained by the imaging means, and determining whether or not it is a living body based on this motion information.
Biometric authentication device comprising a.
And the determining means checks whether or not the subject is a living body by comparing the movement information indicating the stretching of the subject's living body and the movement information collected from the plurality of persons.
Light irradiation means for irradiating light to the living body;
Measurement means for measuring the movement of the living body using an image having a light irradiation position to the living body by the light irradiation means and distance information indicating the distance of the living body calculated from the reflected light of the living body
A step of shooting a part of a living body which can acquire biological information by imaging;
Extracting the motion information of the living body from the plurality of images obtained by the imaging, and determining whether or not the living body is based on the motion information.
Biometric authentication method comprising a.
A computer-readable recording medium executed by a computer and recording a biometric authentication program using an image acquired from a living body,
An imaging function of shooting a part of a living body in which biometric information can be acquired by imaging;
Determination function of extracting the motion information of the living body from the plurality of images obtained by the imaging, and determining whether or not it is a living body based on this motion information.
And a computer-readable recording medium having a biometric authentication program recorded thereon.
KR1020090113791A 2008-11-27 2009-11-24 Biometric authentication device, biometric authentication method, and computer-readable recording medium having biometric authentication program recorded thereon KR101122971B1 (en)
JPJP-P-2008-303258 2008-11-27
KR20100061353A KR20100061353A (en) 2010-06-07
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