JP6629150B2 - Palm detection device, palm print authentication device, palm detection method, and program - Google Patents

Description

  An embodiment of the present invention relates to a palm detection device, a palm print authentication device, a palm detection method, and a program.

2. Description of the Related Art Biometric authentication is known in which a user's body characteristics are collected and registered in advance, and authentication is performed by comparing information obtained by a sensor when performing authentication. For example, fingerprints, faces, irises, veins, voices, palm prints, and the like are used as body characteristics.
Regarding biometric authentication, the biometric information of the first to third instances is acquired, the authentication feature for matching is extracted from the biometric information of the second and third instances, and the biometric information of the first instance is used. A technique for normalizing the relative positions of the authentication features of the second and third instances and extracting a relative feature indicating the normalized relative positional relationship between the authentication features of the second and third instances is known (for example, And Patent Document 1). In this technique, a fingertip and a palm are separately photographed, and fingertip information is normalized based on an image of the palm. A straight line in the direction of each finger is extracted from the contour near the base of each finger in the palm image, and one fingerprint image is rotated by an angle θ formed by the two straight lines.

  In addition, a technique is known in which a screen is scanned with windows of various sizes, and palm identification processing is performed on an image in each window (for example, see Non-Patent Document 1). In this technique, regarding the identification processing of each window, an image in the window is divided into partial regions, and a luminance gradient direction (a direction in which a pixel with higher luminance exists for each pixel) within each partial region; And a relative color (color difference of each pixel with respect to the reference color) is calculated. Next, a histogram is generated from the co-occurrence in the luminance gradient direction in each partial region and the appearance frequency of each of the relative colors, and is used as a feature vector. From the feature vectors of a large number of correct and incorrect samples, it is determined which of the samples is closer to the palm based on the identification boundary obtained by learning in advance, thereby identifying the palm.

JP 2013-200673 A

Tsukasa Ike, Toshinobu Nakasu, Ryuzo Okada "Hand Gesture User Interface with Natural Hand Gesture", Toshiba Review, Vol. 67, no. 6 (2012), P36-39

In the technique described in Patent Literature 1, since the palm image and the fingertip image are separately captured, separate hardware for capturing each of the palm image and the fingertip image is required. Further, the cost of processing for normalizing the fingertip image based on the palm image is high.
Further, in the technique described in Non-Patent Document 1, it is necessary to set windows of various sizes and scan the screen, and it takes a processing time of the CPU from holding a hand to recognition on the screen. In the window, the calculated feature vector is applied to a classifier to identify whether it is a palm. A huge number of palm samples are needed to evaluate the training samples and similarity. Considering that the palm image is biometric information, it is not preferable from the viewpoint of personal privacy when introducing another company or the like.

  The present invention has been made in view of the above problems, and has as its object to provide a palm detecting device capable of detecting a palm without using dedicated hardware.

(1) One aspect of the present invention is a feature information detection unit that detects an edge from an input image, and a color difference signal at one or a plurality of positions of the edge based on the edge detected by the feature information detection unit. A selection unit for selecting an edge corresponding to between fingers, and a region for setting a determination region to be used for specifying a determination target from the input image based on the edge corresponding to between the fingers selected by the selection unit. A palm detection device, comprising: a setting unit; and an output unit that outputs image information included in the determination region set by the region setting unit in the input image.

(2) One aspect of the present invention is the palm detection device according to (1), wherein the smoothing unit smoothes the input image, and the input image smoothed by the smoothing unit is binary-coded using a threshold. A binarization unit that converts the input image into a binary image. The feature information detection unit is a palm detection device that detects an edge from the input image binarized by the binarization unit.

( 3 ) In one aspect of the present invention, in the palm detection device according to the above ( 1 ) or ( 2 ), the selection unit may select any two of the plurality of edges detected by the feature information detection unit. This is a palm detection device that selects an edge corresponding to between fingers based on a color difference signal between edges of a book.

( 4 ) In one aspect of the present invention, in the palm detection device according to the above ( 3 ), the selection unit may be configured to select between any two edges among the plurality of edges detected by the feature information detection unit. Is a palm detection device that selects an edge corresponding to between fingers based on the luminance of a palm.

( 5 ) In one aspect of the present invention, in the palm detection device according to any one of ( 1 ) to ( 4 ), the position of one end of an edge corresponding to between the fingers selected by the selection unit is determined. This is a palm detection device that corrects the position of the base of the finger.

( 6 ) One aspect of the present invention is the palm detection device according to (2 ), further including a noise removing unit that removes a noise component from the input image smoothed by the smoothing unit, and the binarizing unit. Is a palm detection device that binarizes the input image from which noise components have been removed by the noise removal unit using the threshold value.

( 7 ) In one aspect of the present invention, in the palm detection device according to any one of ( 1 ) to ( 5 ), the region setting unit may correspond to a position between the fingers selected by the selection unit. A palm detection device that sets a center of the determination area based on an edge to be performed.

( 8 ) According to an aspect of the present invention, in the palm detection device according to any one of (1) to ( 7 ), the feature information detection unit may be configured to perform a determination based on a position where a palm print of a person is displayed. It is a palm detection device that detects characteristic information.

( 9 ) One aspect of the present invention is a feature information detecting unit that detects an edge from an input image, and a color difference signal at one or a plurality of positions of the edge from the edge detected by the feature information detecting unit. A selection unit for selecting an edge corresponding to between fingers,
From the input image , based on an edge corresponding to between the fingers selected by the selection unit, from the input image, a region setting unit that sets a determination region used to specify a determination target, among the input image, the region setting unit A palmprint authentication device comprising: an authentication unit that specifies a person based on a palmprint image included in the set determination area.

( 10 ) One embodiment of the present invention is a step of detecting an edge from an input image, and detecting a distance between fingers based on a color difference signal at one or more positions of the edge from the edge detected in the detecting step. Selecting an edge corresponding to the finger, and setting a determination area to be used for specifying a determination target from the input image based on the edge corresponding to between the fingers selected in the selecting step; and Outputting the image information included in the determination area set in the step of setting the determination area in the image.

( 11 ) One embodiment of the present invention provides a computer of a palm detection device, wherein an edge is detected from an input image, and a color difference at one or a plurality of positions of the edge is detected from the edge detected in the detecting step. Selecting an edge corresponding to between the fingers based on the signal;
Setting, from the input image, a determination area used for specifying a determination target based on an edge corresponding to the area between the fingers selected in the selecting step; and setting the determination area in the input image And outputting the image information included in the determination area set in the step.

  According to the embodiment of the present invention, a palm can be detected without using dedicated hardware.

It is a figure showing the outline of the palm detecting device concerning an embodiment. It is a figure showing an example of functional composition of a palm detection device concerning an embodiment. It is a figure showing an example of an image before and after smoothing. It is a figure showing the edge detection processing performed by the palm detecting device concerning an embodiment. It is a figure showing the score calculation processing (the 1) performed by a score calculation part. It is a figure showing the score calculation processing (the 2) performed by a score calculation part. It is a figure showing the score calculation processing (the 3) performed by a score calculation part. FIG. 5 is a diagram illustrating a correction process performed by a correction unit. FIG. 9 is a diagram illustrating an area setting process performed by an area setting unit. It is a flowchart which shows an example of operation | movement of the palm detection apparatus which concerns on embodiment. It is a figure showing the example of utilization of the palm detecting device concerning an embodiment. It is a flowchart which shows an example of operation | movement of the utilization example of the palm detection apparatus which concerns on embodiment. It is a figure showing an example of a palm detection device concerning a modification.

<Embodiment>
<Palm detection device>
FIG. 1 is a diagram illustrating an example of an outline of a palm detection device according to an embodiment.
The palm detection device 1 includes an imaging unit 200 and an irradiation unit 300.
An example of the palm detecting device 1 is a portable personal computer such as a mobile phone, a smartphone, a tablet personal computer, or a stationary personal computer. An example of the imaging unit 200 included in the palm detection device 1 is a camera. In addition, an example of the irradiation unit 300 included in the palm detection device 1 is, for example, a flash.
The palm detection device 1 captures an image of the subject TG in a state where light is irradiated from the irradiation unit 300 or in a state where the light is not irradiated, and determines whether or not the subject TG is a palm based on the generated image. I do.
Hereinafter, an outline of the irradiation unit 300 and the imaging unit 200 will be described.

The irradiation unit 300 irradiates light to the subject TG. An example of light emitted from the irradiation unit 300 to the subject TG is, for example, visible light flash emitted from a flash.
The imaging unit 200 captures an image of the subject TG and generates an image P. Specifically, the imaging unit 200 captures an image of the subject TG irradiated with light from the irradiation unit 300 or the subject TG not irradiated with light from the irradiation unit 300, and generates an image P. An example of the subject TG is a person (person). Specifically, as shown in FIG. 1, the subject TG is a human palm. The imaging unit 200 captures an image of a palm, which is the subject TG, and generates an image P.

<Functional configuration of palm detection device 1>
FIG. 2 is a diagram illustrating an example of a functional configuration of the palm detection device according to the embodiment.
The palm detection device 1 includes a control unit 100, an imaging unit 200, and an irradiation unit 300.
The control unit 100 includes a CPU (Central Processing Unit). The control unit 100 includes an acquisition unit 102, an imaging control unit 104, an irradiation control unit 106, a smoothing unit 108, a noise removal unit 110, a binarization unit 112, an edge detection unit 114, a labeling processing unit 116, a score calculation unit 118, An edge selection unit 119, a correction unit 120, an area setting unit 122, and an output unit 124 are provided as functional units.

  The imaging control unit 104 controls the imaging unit 200. The imaging unit 200 captures an image of the subject TG based on the control of the imaging control unit 104, and outputs the generated image P0 to the control unit 100. The imaging unit 200 captures an image of the palm that is the subject TG, and generates an image P0. An example of the image P0 is expressed in a color space such as sRGB, Adobe RGB, or the like.

The irradiation control unit 106 controls the irradiation unit 300. The irradiation unit 300 irradiates the subject TG with light based on the control of the irradiation control unit 106.
Upon obtaining the image P0 output by the imaging unit 200, the obtaining unit 102 outputs the obtained image P0 to the smoothing unit 108 and the score calculation unit 118.

When acquiring the image P0 output by the acquiring unit 102, the smoothing unit 108 converts the acquired image P0 into a grayscale image P1. Specifically, the smoothing unit 108 represents one pixel of the image P0 with 8 bits. These 8 bits do not include color information but include brightness information. Then, in the image P1, the shading is expressed up to 2 to the eighth power (256) gradations. For example, the pixel value 0 is black and the pixel value 255 is white.
Then, the smoothing unit 108 smoothes the image P1. The smoothing unit 108 performs smoothing of the image P1 using a smoothing filter such as a moving average filter and a Gaussian filter. Specifically, the smoothing unit 108 uses a moving average filter to average the luminance value of the pixel of interest and the luminance values of pixels around the pixel of interest to obtain the luminance value of the processed image. Performs the smoothing of the image P1. Further, the smoothing unit 108 uses a moving average filter to increase the weight when calculating the average value as the pixel is closer to the pixel of interest, and use a Gaussian distribution function to reduce the weight as the distance increases. Is calculated to smooth the image P1. The smoothing unit 108 outputs the image P2 obtained by smoothing the image P1 to the noise removing unit 110.
3 shows an example of an image before and after smoothing, (1) shows an example of an image P1, and (2) shows an example of an image P2. Returning to FIG. 2, the description will be continued.

  When acquiring the image P2 output by the smoothing unit 108, the noise removing unit 110 performs a process of removing a noise component from the image P2. The noise removing unit 110 performs a process of removing a noise component from the image P2 by performing image processing after expanding or contracting the image P2. Specifically, when the image P2 represented in gray scale is expanded, the noise removing unit 110 removes a noise component by replacing the maximum luminance value near the target pixel with the luminance value of the target pixel. Perform processing. Further, when the image P2 represented in gray scale is contracted, the noise removing unit 110 replaces the maximum luminance value near the target pixel with the minimum luminance value of the target pixel to remove the noise component. Do.

FIG. 4A illustrates an example of an image P3 obtained by expanding the white pixels of the image P2 by the noise removing unit 110. According to FIG. 4A, since the maximum luminance value in the vicinity of the pixel of interest is replaced by the luminance value of the pixel of interest, the outline of the palm is shown thick.
FIG. 4B illustrates an example of an image P4 obtained by contracting white pixels of the image P3 by the noise removing unit 110. According to FIG. 4B, since the maximum luminance value in the vicinity of the target pixel has been replaced with the minimum luminance value of the target pixel, the outline of the palm is represented thin.
FIG. 4C shows an example of an image P5 obtained by expanding white pixels of the image P4 by the noise removing unit 110. According to FIG. 4C, since the maximum luminance value in the vicinity of the pixel of interest is replaced by the luminance value of the pixel of interest, the outline of the palm is shown thick.
The noise removing unit 110 outputs the image P5 obtained by removing the noise component to the binarizing unit 112.

Upon acquiring the image P5 represented by the gray scale output by the noise removing unit 110, the binarizing unit 112 converts the image P5 into two gray levels of white and black. Specifically, the binarization unit 112 replaces the pixel value with white if the pixel value is higher than the pixel value threshold and black if the pixel value is lower than the pixel value threshold based on a preset pixel value threshold. I do.
FIG. 4D shows an image obtained by converting the image P5 shown in FIG. 4C into two gradations of white and black. The binarization unit 112 outputs an image P6 obtained by converting the image P5 into two gradations of white and black to the edge detection unit 114.

  Upon acquiring the image P6 output by the binarizing unit 112, the edge detection unit 114 detects feature information such as edges from the image P6. Specifically, the edge detection unit 114 detects an edge by detecting a portion where the brightness (luminance) of the image P6 changes discontinuously. The edge detection unit 114 performs edge detection on the image P6 to obtain a continuous curve indicating the boundary of the object. The edge detection unit 114 outputs an image P7 including a result of detecting an edge from the image P6 to the labeling processing unit 116.

When acquiring the image P7 output by the edge detection unit 114, the labeling processing unit 116 assigns a label to an edge included in the image P7. Specifically, the labeling processing unit 116 selects a plurality of continuous pixels included in the curve from the pixels represented in white included in the image P7. Here, when the curve includes a portion where the curvature is equal to or greater than the curvature threshold, the labeling processing unit 116 divides the curve at a point where the curvature is maximum. With this, the labeling processing unit 116 determines that even if the pixel represented by white corresponding to the fingertip and the pixel represented by white corresponding to the side surface of the finger are continuous and represented by one edge, Can be split. In addition, the labeling processing unit 116 determines that the pixel represented by white corresponding to the base of the finger and the pixel represented by white corresponding to each of the side surfaces of two fingers continuous with the base of the finger are continuous. Can be divided even if it is represented by one edge. Then, the labeling processing unit 116 assigns a different label to each of the divided curves.
FIG. 4 (5) shows an example of an edge to which a label is added by the labeling processing unit 116, and shows a state before the curve is separated based on the curvature of the curve. In FIG. 4 (5), in order to make it easier to see the edge with the label, the white and black of the image represented by the two gradations of the image P7 are inverted, and the label assigned to the edge with a different line type is added. expressed.
The labeling processing unit 116 outputs the image P8 including the edge with the label to the score calculation unit 118.

When acquiring the image P0 output by the acquisition unit 102 and the image P8 output by the labeling processing unit 116, the score calculation unit 118 determines whether or not the subject TG is a palm based on the images P0 and P8. Calculate a score for selecting an edge to be used in the processing. Note that the image P0 is expressed in a color space such as sRGB, Adobe RGB, or the like.
Prior to the score calculation, the score calculation unit 118 deletes a specific edge from the labeled edges included in the image P8.

<Edge deletion method (1)>
The score calculation unit 118 leaves the edge included in the image P8 if the length is equal to or longer than the edge length threshold, and deletes the edge if the length is shorter than the edge length threshold.
<Edge deletion method (2)>
The score calculation unit 118 calculates, from the image P0, the average value of the color difference signals (Cr, Cb) of the two regions separated by the edge included in the image P8, and calculates the average value of the color difference signals (Cr, Cb). If the difference is equal to or larger than the first color difference signal threshold, the edge is deleted, and if the difference is smaller than the first color difference signal threshold, the edge is left. For example, the score calculation unit 118 calculates an average value of the color difference signals (Cr, Cb) for a predetermined region included in each of two regions separated by the edge included in the image P8.
<Edge deletion method (3)>
The score calculation unit 118 calculates, from the image P1, the average value of the color difference signals (Cr, Cb) in the area including one end of the edge and the average value of the color difference signals (Cr, Cb) in the area including the other end of the image P8. The edge is deleted if the difference between the average values of the color difference signals (Cr, Cb) is equal to or larger than the second color difference signal threshold, and the edge is left if the difference is smaller than the second color difference signal threshold.

The score calculation unit 118 performs at least one processing of <edge deletion method (part 1)>-<edge deletion method (part 3)> to reduce the number of edges remaining as a result of deleting a specific edge. If the number of edges is equal to or more than the threshold value, it is determined that the edge detection by the edge detection unit 114 is successful, and if it is less than the edge threshold value, it is determined that the edge detection by the edge detection unit 114 has failed. If the score calculation unit 118 determines that the edge detection has failed, the score calculation unit 118 may perform a predetermined error process.
When the score calculation unit 118 determines that the edge detection by the edge detection unit 114 is successful, the score calculation unit 118 selects an edge indicating a finger interval from the remaining edges. Specifically, the score calculation unit 118 selects an edge between the index finger and the middle finger, an edge between the middle finger and the ring finger, and an edge between the ring finger and the little finger.
The score calculation unit 118 obtains a score by performing the following calculation processes (1) to (3) in order to select an edge indicating the interval between fingers. Here, the score calculation unit 118 converts the color space of the image P0 output from the acquisition unit 102 from sRGB to YUV. Here, YUV is a color space expressed using the luminance signal Y and two color difference signals. Then, the score calculation unit 118 selects an edge indicating the distance between the fingers based on the score.

<Score calculation processing (1)>
FIG. 5 is a diagram illustrating a score calculation process (part 1) performed by the score calculation unit. The score calculation unit 118 calculates the color difference signals (Cr, Cb) of the pixels in the region including each of the base positions of the plurality of fingers from the image P1. Then, the score calculation unit 118 calculates the difference between the color difference signals (Cr, Cb).
Specifically, the score calculation unit 118 sets the color difference signal of the pixel in the area including the position corresponding to the base of the edge finger between the index finger and the middle finger as p1. Further, the score calculation unit 118 sets the color difference signal of the pixel in the region including the position corresponding to the base of the finger at the edge between the middle finger and the ring finger as p2. Further, the score calculation unit 118 sets the color difference signal of the pixel in the region including the position corresponding to the base of the finger at the edge between the ring finger and the child finger as p3. The score calculation unit 118 calculates the first score by calculating the differences (p1-p2), (p2-p3), and (p3-p1) of the color difference signals (Cr, Cb).

<Score calculation process (2)>
FIG. 6 is a diagram illustrating a score calculation process (part 2) performed by the score calculation unit. For each of the plurality of edges, the score calculation unit 118 calculates a color difference signal (Cr, Cb) of a pixel in a region including the position of the root of the finger, which is one end of the edge, and a color difference signal of a pixel in a region including the midpoint of the edge. And signals (Cr, Cb). Then, the score calculation unit 118 calculates the difference between the color difference signal (Cr, Cb) of the pixel in the area including the position of the base of the finger and the color difference signal (Cr, Cb) in the area including the midpoint of the edge. The second score is obtained by the calculation.
Specifically, the score calculation unit 118 sets the color difference signal of the pixel in the region including the base position of the finger of the edge between the index finger and the middle finger to s1, and sets the color difference signal of the pixel in the region including the midpoint of the edge to s1. S2. Further, the score calculation unit 118 sets the color difference signal of the pixel in the region including the base position of the finger of the edge between the middle finger and the ring finger to s3, and sets the color difference signal of the pixel in the region including the midpoint of the edge to s4. I do. Further, the score calculation unit 118 sets the color difference signal of the pixel in the region including the base position of the finger of the edge between the ring finger and the child finger to s5, and sets the color difference signal of the pixel in the region including the midpoint of the edge to s6. And The score calculation unit 118 calculates a difference (s1-s2), (s3-s4), and (s5-s6) between the color difference signals (Cr, Cb) to obtain a second score.

<Score calculation process (3)>
FIG. 7 is a diagram illustrating a score calculation process (part 3) performed by the score calculation unit. The score calculation unit 118 defines an intermediate line that does not intersect with both edges between the edge (line1) between the index finger and the middle finger and the edge (line2) between the middle finger and the ring finger. The score calculation unit 118 calculates a variation in one or both of the luminance Y and the color difference signals (Cr, Cb) on the intermediate line. The score calculation unit 118 also defines an intermediate line that does not intersect both edges between the edge (line2) between the middle finger and the ring finger and the edge (line3) between the ring finger and the child finger. The score calculation unit 118 calculates a third score by calculating the variation between the luminance Y and the color difference signals (Cr, Cb) on the intermediate line.

Specifically, the score calculation unit 118 calculates the color difference signal Pt1 of the pixel in the region including one end of the edge (line1) and the color difference Pt2 of the pixel in the region including the other end, and the color difference signal Pt2 of the pixel in the region including one end of the intermediate line. The color difference signal Pt2 'of the pixel in the area including the signal Pt1' and the other end is obtained by calculating Expressions (1) and (2).
Pt1 ′ = (Pt1 of line1 + Pt1 of Line2) / 2 (1)
Pt2 ′ = (Pt2 of line1 + Pt2 of Line2) / 2 (2)
The score calculation unit 118 sets a part of a line segment connecting Pt1 ′ and Pt2 ′ as an intermediate line.
In addition, the score calculation unit 118 defines an intermediate line that does not intersect with both edges between the edge (line 2) between the middle finger and the ring finger and the edge (line 3) between the ring finger and the child finger, as described above. The same processing as the processing is performed.
The score calculation unit 118 calculates a score based on one or both of the luminance Y and the color difference signals (Cr, Cb) on the intermediate line. Specifically, the score calculation unit 118 calculates the third score by calculating the following equation.

In this equation, “σ” indicates the standard deviation of the luminance Y or the color difference signals (Cr, Cb), “x” indicates an arbitrary position of the intermediate line, “n” indicates the length of the intermediate line, “u” indicates the average value of the pixel values.
The score calculation unit 118 calculates a total score based on at least one of the first score, the second score, and the third score. Then, the score calculating unit 118 determines that the subject TG is not a palm when the total score is equal to or smaller than the score threshold, and determines that the subject TG is a palm when the total score is larger than the score threshold.

Specifically, when the first score is the total score, the score calculation unit 118 calculates the differences (p1-p2), (p2-p3), (p2-p3), and (p3-p1) of the color difference signals (Cr, Cb). Among them, the smaller the value, the higher the score is determined, and the larger the value, the lower the score. Since the color difference signal (Cr, Cb) of the pixel in the region including the base position of the finger is a flesh color for any finger, the difference between the color difference signals (Cr, Cb) is small. , The smaller the value, the higher the score.
When the second score is a total score, the score calculation unit 118 calculates the color difference signal of the pixel in the region including the root position of the finger of the edge and the color difference signal of the pixel in the region including the midpoint of the edge. Among the differences (s1-s2), (s3-s4), and (s5-s6), the smaller the value is, the higher the score is, and the larger the value is, the lower the score is. The color difference signals (Cr, Cb) of the pixels in the region including the root position of the finger, which is one end of the edge, and the color difference signals (Cr, Cb) of the pixels in the region including the midpoint of the edge, regardless of the finger Since the color difference is a flesh color, the difference between the color difference signals (Cr, Cb) is small. Therefore, the score calculation unit 118 determines that the smaller the difference between the color difference signals (Cr, Cb), the higher the score.

In addition, when the third score is a total score, the score calculating unit 118 sets the score as the variation in one or both of the luminance Y and the color difference signals (Cr, Cb) on the intermediate line defined between the edges is smaller. The score is determined to be high, and the score is determined to be lower as the variation is larger. When the intermediate line is defined on the finger, the color of the intermediate line is almost a single skin color, and the variation in one or both of the luminance Y and the color difference signal (Cr, Cb) is small. It is determined that the smaller the score, the higher the score.
When determining that the subject TG is a palm, the score calculation unit 118 outputs at least one of the first score, the second score, and the third score to the edge selection unit 119. When determining that the subject TG is not a palm, the score calculation unit 118 may perform a predetermined error process.
When acquiring at least one of the first score, the second score, and the third score output by the score calculation unit 118, the edge selection unit 119 selects three edges based on the score. I do. Here, the three edges are an edge between the index finger and the middle finger, an edge between the middle finger and the ring finger, and an edge between the ring finger and the child finger. The score calculation unit 118 outputs the image P9 including the three edges selected based on the score to the correction unit 120.

FIG. 8 is a diagram illustrating a correction process performed by the correction unit. Upon acquiring the image P9 output by the score calculation unit 118, the correction unit 120 verifies the position of the base of the finger with respect to each of the three edges, and corrects the length of the edges as necessary.
Specifically, the correction unit 120 is an edge between the index finger and the middle finger, and a line segment connecting the position of the base of the finger of the edge and the position of the base of the finger of the edge between the middle finger and the ring finger. Is changed so that the angle α formed between the index finger and the middle finger is within 90 degrees. The correction unit 120 changes the position of the root of the edge between the index finger and the middle finger by extending the edge between the index finger and the middle finger in the direction of the palm from the fingertip.
In addition, the correction unit 120 forms an edge between the ring finger and the child finger, and a line segment connecting the position of the root of the finger of the edge and the position of the root of the finger of the edge between the middle finger and the ring finger. The position of the base of the finger of the edge between the ring finger and the child finger is changed so that the angle β is within 90 degrees. The correction unit 120 changes the position of the root of the finger of the edge between the ring finger and the child finger by extending the edge between the ring finger and the child finger in the direction of the palm from the fingertip.
The correction unit 120 outputs information indicating an edge between the index finger and the middle finger, an edge between the middle finger and the ring finger, and an edge between the ring finger and the child finger to the region setting unit 122. Here, when the position of the base of the finger is changed, the information indicating the changed edge is output to the area setting unit 122.

FIG. 9 is a diagram illustrating an area setting process performed by the area setting unit. When acquiring the information indicating the edge between the index finger and the middle finger, the edge between the middle finger and the third finger, and the edge between the third finger and the second finger, the region setting unit 122 determines Set the judgment area used to identify the target.
Specifically, the region setting unit 122 sets the position of the root of the finger at the edge between the index finger and the middle finger as Q1. In addition, the region setting unit 122 sets the position of the root of the finger at the edge between the middle finger and the ring finger as Q2. In addition, the region setting unit 122 sets the position of the base of the finger at the edge between the ring finger and the child finger as Q3. The region setting unit 122 obtains an average angle of three edges of an edge between the index finger and the middle finger, an edge between the middle finger and the ring finger, and an edge between the ring finger and the child finger. Then, the area setting unit 122 sets a point at a certain distance from Q2 at the average angle as the center point of the determination area (palmprint area). The region setting unit 122 sets the value obtained by dividing the value obtained by adding the distance between Q1 and Q2 and the distance between Q2 and Q3 by 2 to the thickness of the finger, and doubles the thickness of the finger. Let the distance be a fixed distance. The region setting unit 122 outputs image information included in the determination region to the output unit 124. The image information includes palm print image information.

  When acquiring the image information included in the determination region from the region setting unit 122, the output unit 124 outputs the image information.

<Operation of palm detection device>
FIG. 10 is a flowchart illustrating an example of the operation of the palm detection device according to the embodiment.
In step S1002, the acquisition unit 102 acquires the image P0 from the imaging unit 200, and outputs the acquired image P0 to the smoothing unit 108 and the score calculation unit 118. When acquiring the image P0 output by the acquiring unit 102, the smoothing unit 108 converts the acquired image P0 into a grayscale image P1. The smoothing unit 108 smoothes the image P1. The smoothing unit 108 outputs the image P2 obtained by smoothing the image P1 to the noise removing unit 110. When acquiring the image P2 output by the smoothing unit 108, the noise removing unit 110 performs a process of removing noise from the image P2. The noise removing unit 110 outputs an image P5 obtained by performing the process of removing noise to the binarizing unit 112. Upon acquiring the image P5 output by the noise removing unit 110, the binarizing unit 112 converts the image P5 into two gradations of white and black. The binarization unit 112 outputs an image P6 obtained by converting the image P5 into two gradations of white and black to the edge detection unit 114. Upon acquiring the image P6 output by the binarizing unit 112, the edge detection unit 114 detects feature information such as edges from the image P6. The edge detection unit 114 outputs an image P7 including a result of detecting an edge from the image P6 to the labeling processing unit 116. When acquiring the image P7 output by the edge detection unit 114, the labeling processing unit 116 assigns a label to an edge included in the image P7.

In step S1004, the score calculation unit 118 deletes a specific edge from the edges included in the image P8 before calculating the score.
In step S1006, the score calculation unit 118 determines whether or not the number of remaining edges as a result of deleting the specific edge is equal to or greater than an edge number threshold.
In step S1008, the score calculation unit 118 determines that the detection has failed if the number of remaining edges as a result of deleting the specific edge is equal to or less than the line number threshold. In this case, the score calculation unit 118 may perform a predetermined error process.
In step S1010, the score calculation unit 118 calculates a score when the number of remaining edges as a result of deleting the specific edge is larger than the line number threshold.
In step S1012, score calculation section 118 determines whether or not the score is greater than a score threshold.

In step S1014, when the score is equal to or smaller than the score threshold, the score calculation unit 118 determines that the subject TG is not a palm. When determining that the subject TG is not a palm, the score calculation unit 118 may perform a predetermined error process.
In step S1015, when the score is larger than the score threshold, the score calculation unit 118 determines that the subject TG is a palm. In this case, the edge selection unit 119 selects an edge between the fingers based on the score.
In step S1016, when the correction unit 120 acquires the image P9 output by the edge selection unit 119, the correction unit 120 verifies the position of the root of the finger for each of the three edges, and corrects the position of the root of the finger as necessary. Do.
In step S1018, the region setting unit 122 outputs information indicating the edge between the index finger and the middle finger, the edge between the middle finger and the ring finger, and the edge between the ring finger and the child finger, output by the correction unit 120. Upon acquisition, a palm print area to be used for authentication is set in the palm.
In step S1020, when acquiring the image information included in the palm print area output by the area setting unit 122, the output unit 124 outputs the image information.

<Usage example of palm detection device according to embodiment>
FIG. 11 is a diagram illustrating a usage example of the palm detection device 1 according to the present embodiment. In the usage example shown in FIG. 11, the palmprint authentication device 2 performs an authentication process based on image information included in the palmprint region detected by the palm detection device 1.
The palm detection device 1 and the palm print authentication device 2 are connected via a communication network 50 such as the Internet.

<Palm print authentication device>
The palm print authentication device 2 includes a control unit 400 and a storage unit 500.
The control unit 400 includes a CPU. The control unit 400 includes an input unit 402 and an authentication unit 404 as functional units.
The input unit 402 acquires image information output from the palm detection device 1. Here, the image information is image information of an image included in the area set by the palm detection device 1 as the determination area. The input unit 402 outputs the image information to the authentication unit 404.
Upon acquiring the image information output by the input unit 402, the authentication unit 404 performs authentication based on the characteristic information of the image (palmprint image). Specifically, based on the image and the registered image of the palm print registered in the storage unit 500, the authentication unit 404 obtains a corresponding point at a similar position for each of the registered image and the determination image. The “phase-only correlation function value” is obtained by the “phase-only correlation method”. Here, the “phase-only correlation method” is a technique for calculating how similar the shape of a line or the like is. The authentication unit 404 averages the correlation at each corresponding point to calculate a reference value such as a collation score. That is, the authentication unit 404 specifies a person based on the palm print image.
The storage unit 500 registers an image of a palm print.

<Operation of Usage Example of Palm Detection Device According to Embodiment>
FIG. 12 is a flowchart illustrating an example of an operation of a usage example of the palm detection device according to the embodiment.
In step S1202, the input unit 402 acquires image information output by the palm detection device 1.
In step S1204, when acquiring the image information from the input unit 402, the authentication unit 404 performs authentication based on the feature information of the image.

In the above-described embodiment, the case where the light emitted from the irradiation unit 300 to the subject TG is a flash is described, but the invention is not limited to this. The irradiating unit 300 may irradiate the subject TG with light for a longer time than the flashing time, as long as the imaging unit 200 can generate the image P0.
In the above-described embodiment, a case has been described where the light emitted from the irradiation unit 300 to the subject TG is visible light, but the invention is not limited to this. For example, the palm detection device 1 may include, as the irradiation unit 300, a device that irradiates the subject TG with light other than visible light, such as near infrared light, in addition to a flash using visible light.
In the above-described embodiment, a case has been described in which it is determined whether or not the subject TG is a palm based on an image captured by the imaging unit 200. However, the present invention is not limited to this example. For example, it may be determined whether or not the image stored in the palm detection device 1 is a palm.
In the above-described embodiment, a case has been described in which the noise removal unit 110 sequentially performs expansion, contraction, and expansion on a white image of the image P2, but the present invention is not limited to this example. For example, the noise removing unit 110 may perform only expansion or only contraction on the white image of the image P2. In addition, the noise removing unit 110 can appropriately set the number of times of expansion and the number of times of contraction for white pixels of the image P2.

In the above-described embodiment, for each of the plurality of edges, the color difference signal (Cr, Cb) of the pixel including the position of the base of the finger and the color difference signal of the pixel of the region including the midpoint of the edge are calculated by the score calculation unit 118. Although the case of calculating the difference between (Cr, Cb) and (Cr, Cb) has been described, the present invention is not limited to this example. For example, for each of the plurality of edges, the score calculation unit 118 calculates a color difference signal (Cr, Cb) of a pixel in a region including the position of the fingertip and a color difference signal (Cr, Cb) of a pixel in a region including a midpoint of the edge. May be calculated.
In the above-described embodiment, the score calculation unit 118 defines an intermediate line that does not intersect with both edges between the edge between the index finger and the middle finger and the edge between the middle finger and the ring finger, and sets the luminance on the intermediate line. Although the case has been described in which the variation of either or both of the Y and color difference signals (Cr, Cb) is calculated, the present invention is not limited to this example. For example, the score calculation unit 118 calculates the luminance Y and the color difference signal (Cr, Cb) of the region between the edge between the index finger and the middle finger and the edge between the middle finger and the ring finger without defining the intermediate line. Either or both variations may be calculated. The same applies to the edge between the middle finger and the ring finger and the edge between the ring finger and the child finger.

In the above-described embodiment, the score calculation unit 118 is used to determine whether the subject TG is a palm based on the image P0 output from the acquisition unit 102 and the image P8 output from the labeling processing unit 116. Although the case where the score for selecting the edge to be calculated is described, the present invention is not limited to this example. For example, the smoothing unit 108 outputs the image P0 and the image P2 to the noise removing unit 110, the noise removing unit 110 outputs the image P0 and the image P5 to the binarizing unit 112, and the binarizing unit 112 outputs the image P0 And the image P6 to the edge detection unit 114, the edge detection unit 114 outputs the images P0 and P7 to the labeling processing unit 116, and the labeling processing unit 116 outputs the images P0 and P8 to the score calculation unit 118. It may be. Then, score calculating section 118 calculates a score for selecting an edge used when determining whether or not subject TG is a palm based on image P0 and image P8 output from labeling processing section 116. You may make it.
In the above-described embodiment, the case where the palm detection device and the palm print authentication device are connected via the communication network has been described, but the present invention is not limited to this example. For example, the palm detection device and the palm print authentication device may be directly connected by wire or wirelessly.
In the above-described embodiment, a case has been described in which the palm detection device and the palm print authentication device are configured by different devices, but the invention is not limited to this example. For example, it may be configured as an authentication device including a palm detection device and a palm print authentication device.

According to the palm detection device 1 according to the embodiment, whether or not the subject is a palm is determined based on the characteristic information of the subject TG. Therefore, the determination target is the palm without using dedicated hardware. Can be determined. Specifically, based on an image of a subject captured by a camera of a terminal device such as a smartphone, whether or not the subject is a palm, without using separate hardware for capturing each of a palm image and a fingertip image. Can be determined.
Further, since the palm image and the fingertip image are not separately captured, the cost of the process of normalizing the fingertip image based on the palm image is unnecessary. In addition, since the processing is performed by one image capture, the processing time of the CPU from holding the hand to recognizing on the screen is reduced compared to the method of setting various size windows and scanning the screen. it can. Further, since it is determined whether or not the subject is a palm based on the characteristic information of the subject TG, a learning sample is unnecessary. In addition, by converting RGB (sRGB) image data into YUV image data and then performing authentication based on the characteristic information of the determination image included in the determination area, it is possible to identify the illumination robustly to a change in illumination.

<Modification>
FIG. 13 is a diagram illustrating an example of a palm detection device according to a modification.
In the palm detection device according to the modified example, the PC1 executes the processing of the imaging unit 200 and the irradiation unit 300 described above, and the PC2 executes the processing performed by the control unit 100, the control unit 400, and the storage unit 500 described above. It was done. In this example, PC1 and PC2 are connected via a network N capable of transmitting and receiving information.
The PC 1 includes an imaging unit 200 and an irradiation unit 300. In the PC 1, the imaging unit 200 captures an image of the subject TG, and generates an image P0. PC1 transmits the image information of the image P0 to PC2 via the network N. The PC 2 determines whether or not the subject TG is a palm based on the image information of the image P0 received from the PC 1.

Here, an example of the PC 1 is an automated teller machine (ATM). An example of the PC 2 is an ATM centralized monitoring device. That is, the palm detection device according to the modification is implemented in a configuration including an ATM that is an example of the PC1 and a centralized monitoring device that is an example of the PC2. In this case, it is used when performing the biometric authentication of the ATM user in accordance with the authentication of the ATM user such as deposit and deposit.
Specifically, the PC 1 uses the imaging unit 200 and the irradiation unit 300 to image the palm of the user of the ATM, which is the subject TG, and generates the image P0. The PC1 transmits the image information of the image P0 to the centralized monitoring device of the ATM, which is the PC2. The PC 2 determines whether or not the image P0 is a palm image based on the image P0 received from the PC1. Specifically, when the PC 2 determines that the image P0 is a palm image, the PC 2 performs authentication based on the image P0.

  In the modified example, the case has been described in which the PC 1 is an ATM and the PC 2 is a centralized monitoring device of the ATM, but the present invention is not limited to this. For example, PC1 may be a PC and PC2 may be an authentication server. Thus, the authentication server can authenticate the user of the PC when the user of the PC does online shopping and settles the price. When the user of the PC accesses the net bank and conducts a transaction, the authentication server can authenticate the user of the net bank.

  Further, in the modified example, the case where the PC 1 includes the imaging unit 200 and the irradiation unit 300 has been described, but is not limited thereto. The imaging unit 200 and the irradiation unit 300 may be provided in a device different from the PC 1. In this case, a function of transmitting and receiving the image P0 obtained by the imaging unit 200 capturing an image may be provided between the PC 1 and the imaging unit 200. Thereby, the PC 1 can receive the image P0 generated from the imaging unit 200 by the imaging unit 200 capturing the subject TG.

  The embodiments of the present invention and the modifications thereof have been described above. However, these embodiments and the modifications thereof have been presented as examples, and are not intended to limit the scope of the invention. These embodiments and their modifications can be implemented in other various forms, and various omissions, replacements, changes, and combinations can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and their equivalents.

Note that each unit included in the palm detection device and the palm print authentication device according to the embodiment and the modified example may be realized by dedicated hardware, or may be realized by a memory and a microprocessor. Is also good. Each unit included in the palm detection device is configured by a memory and a CPU (Central Processing Unit), and a program for realizing the functions of each unit included in the palm detection device and the palm print authentication device is loaded into the memory and executed. May realize the function. Further, a program for realizing the functions of each unit included in the palm detection device and the palm print authentication device is recorded on a computer-readable recording medium, and the program recorded on this recording medium is read and executed by a computer system. Alternatively, the processing may be performed.
Here, the “computer system” includes an OS and hardware such as peripheral devices.

The “computer system” also includes a homepage providing environment (or a display environment) if a WWW system is used.
The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and a storage device such as a hard disk built in a computer system. Further, a “computer-readable recording medium” refers to a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, which dynamically holds the program for a short time. In this case, it is also assumed that a program that holds a program for a certain period of time, such as a volatile memory in a computer system serving as a server or a client in that case, is included. Further, the above-mentioned program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

The above-described device has a computer inside. The process of each process of each device described above is stored in a computer-readable recording medium in the form of a program, and the above process is performed by reading and executing the program by a computer. Here, the computer-readable recording medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like. The computer program may be distributed to a computer via a communication line, and the computer that has received the distribution may execute the program. Further, the program may be for realizing a part of the functions described above.
Furthermore, what can realize the above-mentioned function in combination with a program already recorded in the computer system, that is, a so-called difference file (difference program) may be used.

  In the above-described embodiments and modifications, the edge detecting unit 114 is an example of a feature information detecting unit, the area setting unit 122 is an example of an area setting unit, the output unit 124 is an example of an output unit, and the smoothing unit is an example. Reference numeral 108 denotes an example of a smoothing unit, and the binarization unit 112 is an example of a binarization unit. Further, the edge selecting unit 119 is an example of a selecting unit, the noise removing unit 110 is an example of a noise removing unit, and the image P0 is an example of an input image.

  DESCRIPTION OF SYMBOLS 1 ... Palm detection apparatus, 2 ... Palm print authentication apparatus, 50 ... Communication network, 100 ... Control part, 102 ... Acquisition part, 104 ... Imaging control part, 106 ... Irradiation control part, 108 ... Smoothing part, 110 ... Noise removal part Reference numeral 112, a binarization unit, 114, an edge detection unit, 116, a labeling processing unit, 118, a score calculation unit, 120, a correction unit, 122, an area setting unit, 124, an output unit, 200, an imaging unit, 300, irradiation Unit, 400 control unit, 402 input unit, 404 authentication unit, 500 storage unit

Claims (11)

A feature information detection unit that detects an edge from the input image;
From the edge detected by the feature information detection unit, based on a color difference signal of one or more positions of the edge, a selection unit that selects an edge corresponding to between fingers,
Based on an edge corresponding to between the fingers selected by the selection unit, from the input image, an area setting unit that sets a determination area used to specify a determination target,
And an output unit that outputs image information included in the determination area set by the area setting unit in the input image. A smoothing unit for smoothing the input image,
A binarizing unit that binarizes the input image smoothed by the smoothing unit using a threshold.
The palm detection device according to claim 1, wherein the feature information detection unit detects an edge from the input image binarized by the binarization unit. The said selection part selects the edge corresponding to between fingers based on the color difference signal between arbitrary two edges among the some edge detected by the said characteristic information detection part. Item 3. A palm detecting device according to Item 2 . The selection unit, among the plurality of edges detected by the feature information detection section, based on the luminance between any two edges, selecting the edge corresponding to between fingers according to claim 3 Palm detection device. The selection unit corrects the position of one end of the edge corresponding to between the finger selected to be the position of the base of the finger, palm detecting apparatus according to any one of claims 1 to 4. A noise removing unit that removes a noise component from the input image smoothed by the smoothing unit
With
The palm detection device according to claim 2, wherein the binarization unit binarizes the input image from which the noise component has been removed by the noise removal unit using the threshold . The region setting unit, based on the corresponding edges between the fingers selected by the selecting unit sets the center of the judgment region, the palm detection according to any one of claims 1 to 5 apparatus. The palm detection device according to any one of claims 1 to 7 , wherein the feature information detection unit detects feature information based on a position where a palm print of a person is represented . A feature information detection unit that detects an edge from the input image;
From the edge detected by the feature information detection unit, based on a color difference signal of one or more positions of the edge, a selection unit that selects an edge corresponding to between fingers,
Based on an edge corresponding to between the fingers selected by the selection unit, from the input image, an area setting unit that sets a determination area used to specify a determination target,
An authentication unit that specifies a person based on a palm print image included in the determination area set by the area setting unit in the input image;
A palm print authentication device comprising: Detecting edges from the input image;
From the edge detected in the detecting step, based on a color difference signal of one or more positions of the edge, selecting an edge corresponding to between the fingers,
Based on the edge corresponding to between the fingers selected in the selecting step, from the input image, a step of setting a determination area used to specify a determination target,
Outputting, from the input image, image information included in the determination area set in the step of setting the determination area;
Palm detecting method performed by the having, palm sensing device. In the computer of the palm detection device,
Detecting edges from the input image;
From the edge detected in the detecting step, based on a color difference signal of one or more positions of the edge, selecting an edge corresponding to between the fingers,
Based on the edge corresponding to between the fingers selected in the selecting step, from the input image, a step of setting a determination area used to specify a determination target,
Outputting, from the input image, image information included in the determination area set in the step of setting the determination area;
To run the program.