Abstract:
A biometrics authentication device is configured so as to include: a PLDI (Principal-Line Dependent Index) generating unit that extracts directional features that respectively correspond to directions different from each other from an image, and generates a PLDI (Principal-Line Dependent Index) indicating dependency of a principal line on the basis of the directional features; a PLDI (Principal-Line Dependent Index) matching processing unit that determines a second degree of similarity between the PLDI and a registered PLDI; and a determining unit that determines identity by using a first degree of similarity.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application is continuation application of International Application PCT/JP2014/058383 filed on Mar. 25, 2014 and designated the U.S., the entire contents of which are incorporated herein by reference. 
     
    
     FIELD 
       [0002]    Embodiments of the present disclosure relate to a technology for biometrics authentication. 
       BACKGROUND 
       [0003]    In an existing biometrics authentication device, when biological information extracted from a photographed image and registered biological information match each other, identity is determined. The biological information includes features denoting a palm print, a vein, and the like, and when biometrics authentication is performed by using the feature denoting the vein, the feature denoting the palm print needs to be separated from the photographed image such that as far as possible only the feature denoting the vein is included in the biological information. As an example of a method for separating the feature denoting the palm print, a method for optically separating the feature denoting the palm print by using a polarizing filter is known. As another example of the method, a method using plural-wavelength photographing is known. 
         [0004]    Related Art Document: A. Ross, A. K. Jain, and J. Reisman, “A Hybrid fingerprint matcher”, Pattern Recognition, vol. 36, no. 7, pp. 1661-1673, 2003. 
       SUMMARY 
       [0005]    A biometrics authentication device according to an embodiment of the present disclosure includes: a PLDI (Principal-Line Dependent Index) generating unit that extracts directional features that respectively correspond to directions different from each other from an image that has been input, and generates a PLDI (Principal-Line Dependent Index) indicating dependency of a principal line on the basis of the directional features; a PLDI (Principal-Line Dependent Index) matching processing unit that determines a first degree of similarity between the PLDI and a registered PLDI stored in a storing unit; and a determining unit that determines identity by using the first degree of similarity. 
         [0006]    Further, a biometrics authentication method according to an embodiment of the present disclosure includes: extracting, by a computer, directional features that respectively correspond to directions different from each other from an image that has been input, and generating a PLDI (Principal-Line Dependent Index) indicating dependency of a principal line on the basis of the directional features; determining, by the computer, a first degree of similarity between the PLDI and a registered PLDI stored in a storing unit; and determining, by the computer, identity by using the first degree of similarity. 
         [0007]    Furthermore, a non-transitory computer-readable recording medium according to an embodiment of the present disclosure which records a program causes a computer to execute a process including: extracting directional features that respectively correspond to directions different from each other from an image that has been input, and generating a PLDI (Principal-Line Dependent Index) indicating dependency of a principal line on the basis of the directional features; determining a first degree of similarity between the PLDI and a registered PLDI stored in a storing unit; and determining identity by using the first degree of similarity. 
         [0008]    The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
         [0009]    It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0010]      FIG. 1  illustrates an example of a biometrics authentication device according to an embodiment. 
           [0011]      FIG. 2  is a flowchart illustrating a biometrics authentication method according to an embodiment. 
           [0012]      FIG. 3  illustrates an example of a feature extracting unit according to an embodiment. 
           [0013]      FIG. 4  illustrates an example of a matching processing unit according to an embodiment. 
           [0014]      FIG. 5  illustrates an example of hardware of a biometrics authentication device. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0015]      FIG. 1  illustrates an example of a biometrics authentication device according to an embodiment of the present disclosure. 
         [0016]    A biometrics authentication device  1  illustrated in  FIG. 1  includes an image obtaining unit  2 , a region specifying unit  3 , a feature extracting unit  4 , a matching processing unit  5 , a score determining unit  6  (a determining unit), and a storing unit  7 . 
         [0017]    The feature extracting unit  4  includes a non-directional feature generation processing unit  8  and a PLDI (Principal-Line Dependent Index) generation processing unit  9 . 
         [0018]    The matching processing unit  5  includes a non-directional feature matching processing unit  10  and a PLDI matching processing unit  11 . 
         [0019]      FIG. 2  is a flowchart illustrating a biometrics authentication method according to an embodiment of the present disclosure. 
         [0020]    First, the image obtaining unit  2  obtains an image of a hand of a subject (S 1 ). As an example, the image obtaining unit  2  is an imaging device, and the image obtaining unit  2  obtains a captured image of a hand of a subject by using a single-plate image sensor and respective RGB color filters of a Bayer array. 
         [0021]    Then, the region specifying unit  3  specifies a ROI (Region Of Interest) that corresponds to a palm of the subject in the image obtained by the image obtaining unit  2  (S 2 ). 
         [0022]    The non-directional feature generation processing unit  8  then generates a non-directional feature on the basis of an image f of the ROI specified by the region specifying unit  3  (S 3 ). In a case in which filtering S is performed on an image f, the term “non-directional” is defined such that it obtains a result that is almost the same as a result of performing only the filtering S even when image rotation conversion T θ  or inverse conversion T θ   −1  of T θ  at various angles θ is inserted before the filtering S. Stated another way, the term “non-directional” is defined using symbols to be S(f)=T θ   −1 (S(T θ (f))) at an arbitrary angle θ. 
         [0023]    The PLDI generation processing unit  9  generates a PLDI (Principal-Line Dependent Index) on the basis of the image f of the ROI (S 4 ). Note that a line feature is a linear image. 
         [0024]    The non-directional feature matching processing unit  10  determines a degree of similarity between the non-directional feature generated by the non-directional feature generation processing unit  8  and a registered non-directional feature that has been registered in advance and that has been stored in the storing unit  7  (S 5 ). 
         [0025]    The PLDI matching processing unit  11  determines a degree of similarity between the PLDI generated by the PLDI generation processing unit  9  and a registered PLDI that has been registered in advance and that has been stored in the storing unit  7  (S 6 ). 
         [0026]    The score determining unit  6  determines the identity of the subject according to the degree of similarity determined by the non-directional feature matching processing unit  10  and the degree of similarity determined by the PLDI matching processing unit  11  (S 7 ). 
         [0027]      FIG. 3  illustrates an example of the feature extracting unit  4  according to the embodiment of the present disclosure. 
         [0028]    The feature extracting unit  4  illustrated in  FIG. 3  includes a filter  41 , a point-wise maximum selecting unit  42 , a binarizing unit  43 , a skeletonizing unit  44 , and a PLDI extracting unit  45 . 
         [0029]    The filter  41  performs Gabor filtering on an input image f of the ROI in eight respective directions θ (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, and 157.5°) so as to obtain respective filter responses (luminance values) as directional features g θ  (a directional feature g 0 °, a directional feature g 22.5° , a directional feature g 45° , a directional feature g 67.5° , a directional feature g 90° , a directional feature g 112.5° , a directional feature g 135° , and a directional feature g 157.5° ). The number of directions θ set in the filtering is not limited to eight, and may be any number that is greater than or equal to two. The filtering is not limited to Gabor filtering, and may be any type of filtering that has a high filter response to a linear dark portion in respective directions θ in the image f. 
         [0030]    The point-wise maximum selecting unit  42  outputs a maximum directional feature max θ {g θ (i,j)} as a non-directional feature g(i,j) from among the respective directional features g θ (i,j) extracted from the filter  41 , as expressed by Expression 1. The i represents a position in a horizontal-axis direction of two-dimensional coordinates, and the j represents a position in a vertical-axis direction of the two-dimensional coordinates, when positions of respective pixels within the ROI are made to correspond to positions on the two-dimensional coordinates. 
         [0000]        g ( i,j ):=max θ   {g   θ ( i,j )}, ( i,j )εROI  Expression 1
 
         [0031]    The binarizing unit  43  outputs 1 as a non-directional plane feature b(i,j) when the non-directional feature g(i,j) output from the point-wise maximum selecting unit  42  has a positive value, and the binarizing unit  43  outputs 0 as the non-directional plane feature b(i,j) when the non-directional feature g(i,j) has a value that is not a positive value, as expressed by Expression 2. The obtained non-directional plane feature b is stored in the storing unit  7 . 
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         [0032]    In the description above, the binarizing unit  43  has performed binarization by performing simple thresholding using a constant of 0, but the binarizing unit  43  may perform binarization using a more advanced Adaptive-thresholding scheme. 
         [0033]    The skeletonizing unit  44  performs skeletonizing on the non-directional plane feature b so as to obtain a non-directional line feature LF, as expressed by Expression 3. The skel represents skeletonizing. The obtained non-directional line feature LF is stored in the storing unit  7 . 
         [0000]      LF:= skel ( b )  Expression 3
 
         [0034]    The PLDI extracting unit  45  generates a PLDI indicating dependency of a principal line (principally, a line that corresponds to a palm print) within the ROI on the basis of the respective directional features g θ  extracted from the filter  41 . As an example, the PLDI extracting unit  48  selects a maximum directional feature MAX(i,j) from among the respective directional features g θ (i,j) output from the filter  41 . The PLDI extracting unit  48  selects a minimum directional feature MIN(i,j) from among the respective directional features g θ (i,j) output from the filter  41 . The PLDI extracting unit  48  also obtains an average value AVE(i,j) of the respective directional features g θ (i,j) output from the filter  41 . Then, the PLDI extracting unit  48  determines a value obtained by dividing a difference between the maximum directional feature MAX(i,j) and the minimum directional feature MIN(i,j) by the average value AVE(i,j) to be a PLDI(i,j). 
         [0035]    Note that a method for obtaining a PLDI (i,j) is not limited to the method above. 
         [0036]      FIG. 4  illustrates an example of the matching processing unit  5  according to the embodiment of the present disclosure. 
         [0037]    The non-directional feature matching processing unit  10  determines a degree of similarity score 1  between the non-directional line feature LF that has been output from the skeletonizing unit  44  and that has been stored in the storing unit  7  and a registered non-directional line feature TLF that has been registered in advance and that has been stored in the storing unit  7 . 
         [0038]    The PLDI matching processing unit  11  determines a degree of similarity score 2  between the PLDI that has been output from the PLDI extracting unit  45  and that has been stored in the storing unit  7  and a registered PLDI TPLDI that has been registered in advance and that has been stored in the storing unit  7 . 
         [0039]    The score determining unit  6  weights the degrees of similarity score 1  and score 2  by using a constant a k  and a constant c, and the score determining unit  6  determines the sum of the weighted degrees of similarity scorer and score 2  to be a degree of similarity score, as expressed by Expression 4. Then, the score determining unit  6  determines the identity of the subject, for example, when the degree of similarity score is greater than or equal to a threshold. 
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         [0040]    Hereinafter, with respect to a certain k, when a k &gt;0 is established and as an absolute value of the constant a k  becomes relatively greater than another constant a k , it is said that a degree of similarity score k  is positively utilized (an action of positively affirming similarity). Otherwise, it is said that the degree of similarity score k  is negatively utilized. 
         [0041]    The score determining unit  6  applies a smaller weight on the degree of similarity score 2  as the degree of similarity score 2  attains a greater value, so as to negatively utilize the degree of similarity score 2 . 
         [0042]    As described above, by negatively utilizing the degree of similarity score 2 , the identity of a subject can be determined in a state in which an influence of a palm print has been suppressed, and consequently authentication accuracy can be improved. Stated another way, the biometrics authentication device  1  according to the embodiment of the present disclosure can prevent the FAR from increasing even when a method for physically separating a feature denoting the palm print from an image fails to be applied. Further, even when melanin is abnormally deposited, in particular, in a palm of a subject, and a large portion indicating the palm print is included in a non-directional line feature LF, the identity of the subject can be determined in a state in which an influence of the palm print on the non-directional line feature LF has been suppressed, and consequently the FAR can be reduced. 
         [0043]    In the embodiment above, the degrees of similarity score 1  and score 2  are determined, and the identity of a subject is determined by using the degrees of similarity score 1  and score 2 . However, only the degree of similarity score 2  may be determined, and the identity of the subject may be determined by using only the degree of similarity score 2 . This allows the identity of the subject to be determined by using only the degree of similarity score 2 , namely, by using only a feature indicating a palm print, and even when a method for physically separating the feature denoting the palm print from an image fails to be applied, the FAR can be prevented from increasing. 
         [0044]    Further, the degrees of similarity score 1  and score 2  may be determined, the identity of a subject may be determined by using only the degree of similarity score 2  when a PLDI has a great value (for example, when the PLDI has a value greater than or equal to a threshold), and the identity of the subject may be determined by using the degrees of similarity score 1  and score 2  when the PLDI has a small value (for example, when the PLDI has a value smaller than a threshold). By doing this, multimodal authentication in which a plurality of authentication methods (modals) are switched and complementary authentication is performed can be performed. 
         [0045]      FIG. 5  illustrates an example of hardware that configures the biometrics authentication device  1  according to the embodiment of the present disclosure. 
         [0046]    As illustrated in  FIG. 5 , hardware that configures the biometrics authentication device  1  includes a control unit  1201 , a storing unit  1202 , a recording medium reading device  1203 , an input/output interface  1204 , and a communication interface  1205 , and these components are mutually connected via a bus  1206 . The hardware that configures the biometrics authentication device  1  may be implemented by using a cloud or the like. 
         [0047]    As an example, a Central Processing Unit (CPU), a multicore CPU, or a programmable device (a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD) or the like) can be considered to be used as the control unit  1201 . The control unit  1201  corresponds to the region specifying unit  3 , the feature extracting unit  4 , the matching processing unit  5 , and the score determining unit  6  illustrated in  FIG. 1 . 
         [0048]    The storing unit  1202  corresponds to the storing unit  7  illustrated in  FIG. 1 , and examples of the storing unit  1202  include a memory such as a Read Only Memory (ROM) or a Random Access Memory (RAM), and a hard disk. The storing unit  1202  may be used as a work area at the time of execution. In addition, another storing unit may be provided outside the biometrics authentication device  1 . 
         [0049]    The recording medium reading device  1203  is controlled by the control unit  1201  so as to read data recorded in a recording medium  1207  or to write data to the recording medium  1207 . The recording medium  1207  that is removable is a non-transitory computer-readable recording medium, and examples of the recording medium  1207  include a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory. Examples of the magnetic recording device include a hard disk drive (HDD). Examples of the optical disk include a Digital Versatile Disc (DVD), a DVD-RAM, a Compact Disc Read Only Memory (CD-ROM), and a CD-R (Recordable)/RW (ReWritable). Examples of the magneto-optical recording medium include a Magneto-Optical disk (MO). Note that the storing unit  1202  is also included in the non-transitory recording medium. 
         [0050]    The input/output interface  1204  is connected to an input/output unit  1208 , and the input/output interface  1204  transmits information input by a user via the input/output unit  1208  to the control unit  1201  via the bus  1206 . The input/output interface  1204  also transmits information transmitted from the control unit  1201  to the input/output unit  1208  via the bus  1206 . 
         [0051]    The input/output unit  1208  corresponds to the image obtaining unit  2  illustrated in  FIG. 1 , and examples of the input/output unit  1208  include an imaging device. Examples of the input/output unit  1208  also include a keyboard, a pointing device (for example, a mouse), a touch panel, a Cathode Ray Tube (CRT) display, and a printer. 
         [0052]    The communication interface  1205  is an interface for performing Local Area Network (LAN) connection or Internet connection. The communication interface  1205  may be used as an interface for performing LAN connection, Internet connection, or wireless connection with another computer, as needed. 
         [0053]    By using a computer having the hardware above, various processing functions performed by the biometrics authentication device  1  are implemented. In this case, a computer executes a program describing the content of the various processing functions performed by the biometrics authentication device  1  such that the above various processing functions (for example, the region specifying unit  3 , the feature extracting unit  4 , the matching processing unit  5 , and the score determining unit  6 ) are implemented on the computer. The program describing the content of the various processing functions can be stored in the storing unit  1202  or the recording medium  1207 . 
         [0054]    In a case in which a program is distributed, the recording medium  1207  recording the program, such as a DVD or a CD-ROM, is sold, for example. The program can be recorded in a storage of a server computer, and the program can be transferred from the server computer to another computer via a network. 
         [0055]    The computer that executes a program stores, for example, the program recorded in the recording medium  1207  or the program transferred from the server computer in the storing unit  1202 . The computer reads the program from the storing unit  1202 , and performs processing according to the program. The computer may directly read a program from the recording medium  1207 , and may perform processing according to the program. Further, every time a program is transferred from the server computer, the computer may perform processing according to the received program. 
         [0056]    In the embodiment of the present disclosure, an image processing device that performs authentication using a vein of a palm has been described as an example, but the embodiment is not limited to this, and any other feature detection region of a living body may be used. 
         [0057]    The other feature detection region of a living body is not limited to a vein, and as an example may be a vascular image of a living body, a pattern of a living body, a fingerprint or a palm print of a living body, the sole of the foot, a finger or toe, the back of the hand or the instep of the foot, the wrist, the arm, or the like. 
         [0058]    When a vein is used for authentication, the other feature detection region of a living body may be any region in which the vein can be observed. 
         [0059]    The existence of an other feature detection region of a living body in which biological information can be specified is advantageous to authentication. As an example, when a palm, a face or the like is used, a region can be specified from an obtained image. In addition, various modifications to the embodiment above can be made without departing from the gist of the embodiment. Further, multiple variations or modifications to the embodiment above can be made by those skilled in the art, and the embodiment is not limited to the exact configuration and applications described above. 
         [0060]    According to the embodiment of the present disclosure, even when a method for physically separating a feature denoting a palm print from an image fails to be applied, an FAR can be prevented from increasing. 
         [0061]    All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.