CN109460746B - Separation method of palm print ROI - Google Patents
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- CN109460746B CN109460746B CN201811480982.3A CN201811480982A CN109460746B CN 109460746 B CN109460746 B CN 109460746B CN 201811480982 A CN201811480982 A CN 201811480982A CN 109460746 B CN109460746 B CN 109460746B
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1347—Preprocessing; Feature extraction
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- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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Abstract
The divisional application discloses a separation method of palmprint ROI, which belongs to the palmprint recognition field, in order to solve the problems that in the existing palmprint recognition process, the positioning point is not easy to be determined in the positioning segmentation method based on the square of palmprint segmentation and the ROI extraction deviation degree of the same kind of image is large, the contour line of the palm can change along with the opening degree of the fingers, the contour line of the edge area on one side of the little finger of the palm can not change along with the opening degree of the fingers, and the effect is that: the image segmentation algorithm reduces the influence of image rotation and translation during image acquisition.
Description
The invention is a divisional application with the title of 'palm print ROI segmentation method in palm print recognition', application No. 201610409033.0, application date 2016-06-08.
Technical Field
The invention belongs to the field of palm print recognition, and relates to a palm print ROI segmentation method in palm print recognition.
Background
With the development of society and the improvement of technology level, people's safety awareness is continuously enhanced, and information safety is concerned more and more, so that everyone always faces the identification problem in real life. Traditional identity authentication often adopts passwords, certificates and the like, and the traditional authentication methods have great disadvantages. Biometric identification technology is slowly replacing traditional identity authentication methods due to its high inherent security level, often using human features such as fingerprints, faces, irises, gait, handwriting, hand shapes, palmprints, etc. At present, a single biometric feature has inherent limitations, and no single biometric feature recognition technology can meet the actual requirement. The multi-mode biological characteristic recognition technology improves the recognition accuracy and expands the application range by a multi-biological characteristic fusion method so as to meet the actual requirement. The hand image acquisition is convenient, the user acceptance is high, the information content is large, the identification accuracy is high, and the like, so that the hand image acquisition method is widely applied at present.
The palm print recognition generally comprises several main parts of palm print extraction, palm print information analysis and the like, wherein the palm print extraction involves the step of palm print segmentation.
Disclosure of Invention
In order to solve the problems that positioning points are not easy to determine and ROI extraction offset of images of the same type is large in the square-based positioning and segmentation method of palm print segmentation in the existing palm print identification process, the invention provides a palm print ROI segmentation method in palm print identification, which aims to determine the positioning points in the square-based positioning and segmentation method more easily and reduce the ROI extraction offset of the images, and the technical scheme of the invention is as follows:
a palm print ROI segmentation method in palm print recognition comprises the following steps:
s1, selecting a fitting straight line;
s2, image correction and ROI segmentation of the palm print.
Further, a stable straight line is selected from the image to serve as a reference direction of ROI segmentation, and a straight line is fitted by a least square method according to edge points of a first quadrant of the contour map.
Further, determining the center point of the ROI, using a valley point M1 as a fixed point, finding a point M2 'on the line where the point M2 is located, wherein the point M2' makes a fixed angle between the straight line M1M2 and the fitted straight line L, taking the midpoint O of the line segment M1M2 ', making a vertical bisector of the straight line M1M 2', and finding a point O1 with a fixed length R in the right side region of the vertical bisector, so that the point O1 is in the center region of the palmFinally, taking the point O1 as the center point of the ROI, and cutting out a 128 × 128 square area as the ROI of the image
Two sides of the square are parallel to the fitting straight line L.
Further, the step of fitting the straight line is:
let the equation expression of the straight line be:
y＝kx+b (1)
calculating the intercept b of the straight line on the y-axis and the slope k of the straight line according to the measured value of the palm edge profile, (x)i,yi) Coordinates which are measurements of the contour of the palm edge, b0、k0Approximate values for b, k, let:
b＝b0+δb
k＝k0+δk
where δ b and δ k are the deviations of slope and intercept;
taking y as a dependent variable, taking x as an independent variable, and taking an error equation as follows:
the matrix expression of the error equation is:
AδX＝L+V
wherein
By least squares criterion
VTV＝min
Namely, it is
Its least squares solution is:
the values of k and b are obtained by the above steps and are substituted into the formula 1 to obtain a fitting equation and a fitting straight line.
Has the advantages that: the algorithm solves the problems that the positioning points are difficult to determine and the ROI extraction offset degree of the similar images is large in the square-based positioning segmentation method, and the image segmentation algorithm also reduces the influence of image rotation and translation during image acquisition. The algorithm solves the complex problem by a simple method, saves time and is easier to realize under the condition of achieving the same effect compared with other existing methods, and the extracted ROI has smaller deviation degree, is reliable and has higher practicability.
Drawings
FIG. 1 is a schematic diagram of hand shape images and hand shape landmark positions processed by the present invention;
FIG. 2 is a schematic diagram of the disc algorithm of the present invention;
FIG. 3 is a schematic view of a hand-shaped partial block of the present invention;
fig. 4 is a palm print image and an ROI segmentation map.
Detailed Description
Example 1: the most important step in palm print identification is the segmentation of a palm print region of interest (ROI), and a ROI segmentation method based on specific part straight line fitting is provided aiming at the defects of the original algorithm. The contour line of the palm can change along with the opening degree of the fingers, and the contour line of the edge area on one side of the little finger of the palm can not change along with the opening degree of the fingers. According to the characteristic, a straight line L is fitted by adopting a least square method aiming at specific edge points of the palm profile. Taking the straight line L as a reference, and taking two valley points M1 and M2 in fig. 4(a) as reference points, respectively making two straight lines ab and cd parallel to the straight line L; a straight line OO1 parallel to the straight line L is made from the middle point of the points M1 and M2, a straight line perpendicular to the L is made from the point M1, the intersection point of the straight line with the straight line cd is M2', the intersection point of the straight line OO1 is O, and a certain length is determined and intercepted on the straight line OO1 by taking the point O as a reference, so that the point O1 is determined. The length of the cut is determined centering on the point O1, and the images are divided in the directions parallel and perpendicular to the straight line L, respectively, to obtain the palm print ROI, as shown in fig. 4 (a). The embodiment describes a palm print ROI segmentation method in a palm print identification process, which comprises the following steps:
1) selecting a fitted straight line
Firstly, a stable straight line is selected in the image as a reference direction for ROI segmentation. The analysis of the image shows that when the image is collected, although the fingers are opened randomly, the contour line of the rear edge area on one side of the little finger of the palm is changed very little, and according to the characteristic, a straight line is fitted by adopting a least square method aiming at the edge point of the first quadrant of the contour map.
Let the equation expression of the straight line be:
y＝kx+b (1)
the optimal b (intercept of the line on the y-axis) and k (slope of the line) are determined from the measured values of the palm edge profile. (x)i,yi) Coordinates which are measurements of the contour of the palm edge, b0、k0Are approximate values of b and k. Order:
b＝b0+δb
k＝k0+δk
taking y as a dependent variable, taking x as an independent variable, and taking an error equation as follows:
where δ b and δ k are the deviations of slope and intercept;
the matrix expression of the error equation is:
AδX＝L+V
wherein
From the least squares criterion (min represents the minimum)
VTV＝min
Namely, it is
Its least squares solution is:
thus, the values of a and b are obtained, and the fitting equation is obtained by substituting the values into the formula 1, for example, the straight line L in FIG. 4(a) is the obtained fitting straight line.
2) Image correction and ROI segmentation of palm prints
After the palm print image is processed, the central point of the ROI is determined. The following method is adopted to reduce the problem of the displacement of the central point of the image of the same person. As shown in fig. 4(a), a point M2' is found on the line where the point M2 is located, with the valley point M1 as a fixed point, such that the straight line M1M2 forms a fixed angle (90 degrees in the experiment) with the fitted straight line L. The midpoint O of the line segment M1M2 'is taken as the perpendicular bisector of the straight line M1M 2', and the fixed length R (wherein, the fixed length R is found in the right region of the perpendicular bisector)
) Point O1, point O1 is in the central area of the palm, and finally, a 128 × 128 square (where two sides are parallel to the fitting straight line L) area is cut out as the ROI of the image with point O1 as the center point of the ROI. Fig. 4(b) is an experimental simulation diagram of the segmentation of a special image by the improved algorithm.
The embodiment provides a new positioning segmentation algorithm aiming at the defects in the existing method, the algorithm solves the problems that positioning points are not easy to determine and ROI extraction offset of the similar images is large in the square-based positioning segmentation method, and the image segmentation algorithm also reduces the influence of image rotation and translation during image acquisition. The algorithm solves the complex problem by a simple method, saves time and is easier to realize under the condition of achieving the same effect compared with other existing methods, and the extracted ROI has smaller deviation degree, is reliable and has higher practicability.
Example 2: the embodiment discloses a multi-mode biological recognition method based on hand shapes and palm prints, wherein the hand shape recognition comprises the following main parts of hand shape contour extraction, feature point positioning, feature quantity analysis and the like. The palm print recognition generally includes several main parts, such as palm print extraction, palm print information analysis, etc., wherein the palm print extraction involves a step of palm print segmentation. For the palm print part, the technical scheme is as described in the embodiment 1, and for the hand shape part, please refer to the following scheme. The description of the hand shape portion may be an upper step or a lower step described in the palm print portion as a part of the palm print ROI segmentation method.
Carrying out gray level processing on the hand-shaped image to carry out gray level enhancement; determining a segmentation threshold value, and carrying out binarization on the image; by boundary tracing, a hand-shaped contour is extracted as shown in fig. 1. By analyzing fig. 2, a certain point on the contour line is taken as the center of a circle, R is taken as the radius, and pixels belonging to both the target area and the background area exist in the circle. It can be seen that some points of the target area and background area within the disc are above the centre of the circle and some are below the centre of the circle when the disc moves in a straight line. When the disk is turned to the inflection point of the convex region, all points of the target region in the disk are below the center point, and when the disk is turned to the inflection point of the convex region, all points of the background region in the disk are above the center point. A disc extreme value algorithm is proposed based on the theory, the inside of a hand-shaped contour line is a target area, the outside of the hand-shaped contour line is a background area, and it can be seen through analyzing a hand-shaped contour diagram (figure 1) that a point T at the center of a disc at the peak of a certain finger is assumed, points in the neighborhood around the point T are all below or in the same row, similar characteristics are also provided for a finger valley, the only difference is that the points in the neighborhood are above or in the same row of the finger valley point, and only the characteristic points of the finger peak and the finger valley have the characteristic, so that the positions of the peak point and the valley point of the finger are determined.
In fig. 3(a), a small area where the middle finger peak point is located is determined, the middle finger peak point T2 is determined by using a disk extreme method, and the hand-shaped image is divided into two parts by using the column of T2, fig. 3(b) is a sub-image of the area where the little finger of the ring finger is located, and fig. 3(c) is a sub-image of the area where the index finger is located. In fig. 3(b), the area where the valley point between the little finger and the ring finger is located is determined, and the valley point T7 is determined by the disk extremum method. For fig. 3(c), the segmentation parameters are determined and segmented into a sub-graph 3(d) of the region of the valley point between the index finger and the middle finger and a sub-graph 3(e) of the region of the peak point between the index finger and the middle finger. The index and middle finger valley point T5 and the index finger peak point T1 were determined using the disk extremum method in the smaller region of FIG. 3(d) and FIG. 3(e), respectively. And further determining segmentation parameters, and segmenting the graph 3(b) into a middle finger and ring finger valley point region sub graph 3(f), a ring finger peak point region sub graph 3(g) and a little finger peak point region sub graph 3 (h). The disk extremum method is used to determine the valley point T6 between the middle and ring fingers in the smaller area in fig. 3(f), and the disk extremum method is used to determine the peak point T3 of the ring finger and the peak point T4 of the small finger in fig. 3(g) and fig. 3(h), respectively.
And performing graying processing on the hand-shaped image, drawing a histogram of the grayscale image, finding out a pixel grayscale concentration range, and performing grayscale enhancement to make the image clearer. Carrying out binarization by using a local threshold value, carrying out corrosion expansion operation on the binarized image by using a disc with the radius of 1 to remove small areas, then carrying out feature positioning,
in the step of feature location, this embodiment proposes a method for fixing a location sequence of feature points in hand shape recognition, and technical terms in the method are defined as follows: sub-graph b is a sub-graph of the region where the little finger of the ring finger is located, sub-graph c is a sub-graph of the region where the index finger is located, sub-graph e is a sub-graph of the region where the peak point of the index finger is located, sub-graph f is a sub-graph of the valley point between the middle finger and the ring finger, sub-graph g is a sub-graph of the region where the peak point of the ring finger is located, and sub-graph h is a sub-graph of the peak point of the little finger;
the method comprises the following steps:
s1, creating 7 space arrays Si[]Used for storing the characteristic points of the peaks or the valleys of the fingers which meet the condition, wherein: 1, …, 7;
s2, scanning the original drawing a from top to bottom and from left to right, searching a first intersection point of a scanning line and a finger, determining points of all contour lines below the circle center by using a disc extreme value method based on the point and storing the points into an array S1In, array S1The middle point of (a) is the middle finger peak point T2;
s3, dividing the original graph into a sub graph b and a sub graph c according to the middle finger peak point T2, scanning the sub graphs from bottom to top and from left to right, and determining a point storage array S with the contour lines below the circle center by using a disc extreme value method by taking other intersection points of the line except the intersection point with the left edge contour line as reference points when a plurality of intersection points are firstly formed between the scanning line and the contour lines2In, array S2The middle point of (a) is the valley point T7 of the little finger and the ring finger;
s4, calculating
Wherein x2、x7Is the abscissa of T2 and T7, and is given by n for the sub graph c3Scanning the sub-graph e from top to bottom and from left to right for the region of the sub-graph e at the left boundary, searching the first intersection point of the scanning line and the finger, determining the points of the contour line below the circle center by using the disk extreme value method based on the point, and storing the points into an array S3In, array S3The middle point of (a) is the middle finger peak point T2;
s5, calculating
x1On the abscissa of T1, for sub-diagram d, row by y7Upwards, column by x2To n4Scanning the area, searching the first intersection point of the scanning line and the finger, determining the points of the contour line below the center of the circle by using a disc extreme method based on the point, and storing the points into an array S4In, array S4The middle point of (a) is the valley point T5 between the index finger and the middle finger, where y7Is the ordinate of point T7;
s6, calculating
x5On the abscissa of T5, for sub-diagram f, the row is given by y7Upwards, column by n5To x2Scanning the area, searching the first intersection point of the scanning line and the finger, determining the points of the contour line below the center of the circle by using a disc extreme method based on the point, and storing the points into an array S5In, array S5The middle point of (1) is a valley point T6 between the middle finger and the ring finger;
s7, calculating
P to the diagram b with n6Scanning the sub-graph g from top to bottom and from left to right for the area of the sub-graph g at the right boundary, searching the first intersection point of the scanning line and the finger, determining the points of the contour line below the center of the circle by using a disk extreme value method based on the point, and storing the points into an array S6In, array S6The middle point of (1) is the ring finger peak point T3;
s8, calculating y according to the determined pointsmax＝MAX(y1,y3)，ymin＝MIN(y1,y3)，a3＝|y2-yminI, for subgraph h, row by (y)max+a3) Downwards, column by n6Scanning the area for the right boundary, recording the number of lines whose intersection is greater than 2 for the first time, will satisfy | ni-ni+1The intersection point of | ≧ 2 is stored in the array S7In, array S7The middle point of (2) is the feature point T4 of the little finger peak point.
Wherein:
sub-graph b is a sub-graph of the region where the little finger of the ring finger is located, sub-graph c is a sub-graph of the region where the index finger is located, sub-graph e is a sub-graph of the region where the peak point of the index finger is located, sub-graph f is a sub-graph of the valley point between the middle finger and the ring finger, sub-graph g is a sub-graph of the region where the peak point of the ring finger is located, and sub-graph h is a sub-graph of the peak point of the little finger;
n3index finger peak point subgraph segmentation parameter, n4Index finger and middle finger valley point subgraph segmentation parameter, n5Middle finger and ring finger valley point subgraph segmentation parameter, n6And (5) carrying out segmentation parameters on the peak points of the ring finger.
y1，y2，y3The ordinate, y, of the characteristic points T1, T2 and T3, respectivelymaxIs y1And y3Maximum value of, yminIs y1And y3Is measured.
The palm print ROI segmentation method in palm print identification can quickly and effectively extract the palm print ROI by adopting the specific region straight line fitting and fixed characteristic point positioning technology. The defects of the original algorithm are overcome, and the influence of image rotation and translation during image acquisition is reduced. Compared with the original algorithm, the method has the advantages of greatly shortening the calculation time and being easier to realize, along with higher calculation efficiency and accuracy, and provides theoretical and experimental basis for the realization of the palm print-based identity authentication system. The algorithm is high in accuracy, high in speed and simple, solves the problems that the scanning range is large and the disc threshold and the radius are difficult to determine in the traditional method, remarkably improves the characteristic positioning effect, reduces the requirements on image acquisition, improves the comfort of users, has no strict requirements on the opening degree of fingers of an acquired person, and is suitable for users with defects (bending and partial deletion) on the fingers.
In addition, the method for fixing the positioning sequence of the feature points in the hand shape recognition, which is related by the scheme, adopts a hand shape image blocking technology and utilizes a disc extremum algorithm to quickly and accurately extract the hand shape feature points, the algorithm has the advantages of high accuracy, high speed and simple algorithm, solves the problems of large scanning range and difficulty in determining the disc threshold and the radius in the traditional method, obviously improves the feature positioning effect, reduces the requirements on image acquisition, improves the comfort of users, has no harsh requirements on the finger opening degree of the acquired person, and is also suitable for the users with defects (bending and partial missing) on the finger.
The above description is only for the purpose of creating a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the technical scope of the present invention.
Claims (1)
1. A separation method of a palmprint ROI is characterized in that the contour line of a palm can change along with the degree of opening of fingers, the contour line of an edge area on one side of a little finger of the palm can not change along with the degree of opening of the fingers, a straight line L is fitted by adopting a least square method aiming at a specific edge point of the palm contour, and two straight lines ab and cd parallel to the straight line L are respectively made by taking the straight line L as a reference point and taking two valley points M1 and M2 as reference points; taking the middle points of the points M1 and M2 as a straight line OO1 parallel to the straight line L, taking the point M1 as a straight line vertical to the L, taking the intersection point of the straight line and the straight line cd as M2', taking the intersection point of the straight line OO1 as O, taking the point O as a reference, determining a certain length to intercept on the straight line OO1, determining a point O1, taking the point O1 as a center, determining the intercepted length, and separating the images in the directions parallel to and vertical to the straight line L respectively to obtain a palm print ROI;
the determined point O1 is a perpendicular bisector of the straight line M1M 2', and a point O1 with a fixed length R is found in the right side area of the perpendicular bisector, wherein the fixed length R is
The cutting length is to cut a 128 × 128 square area as the ROI of the image.
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| CN106980828B (en) * | 2017-03-17 | 2020-06-19 | 深圳市魔眼科技有限公司 | Method, device and equipment for determining palm area in gesture recognition |
| CN107704846A (en) * | 2017-10-27 | 2018-02-16 | 济南大学 | Palm grain identification method based on two-value direction commensal vector and bloom wave filters |
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| CN111339932B (en) * | 2020-02-25 | 2022-10-14 | 南昌航空大学 | Palm print image preprocessing method and system |
| TWI781459B (en) * | 2020-10-08 | 2022-10-21 | 國立中興大學 | Palm vein feature identification system and method |
| CN113780201B (en) * | 2021-09-15 | 2022-06-10 | 墨奇科技（北京）有限公司 | Hand image processing method and device, equipment and medium |
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| CN109583398A (en) | 2019-04-05 |
| CN109376708A (en) | 2019-02-22 |
| CN105938549A (en) | 2016-09-14 |
| CN109460746A (en) | 2019-03-12 |
| CN105938549B (en) | 2019-02-12 |
| CN109583398B (en) | 2022-11-15 |
| CN109376708B (en) | 2021-11-26 |
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