CN110163119A - A kind of finger vein identification method and system - Google Patents
A kind of finger vein identification method and system Download PDFInfo
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Abstract
本发明公开了一种手指静脉识别方法及系统,包括:确定手指区域的上下边缘点集,细化边缘到一个像素宽度;从细化后的边缘点集中选取合适的点进行边缘扩展,获得真实边缘点集;根据获得像素点坐标,对手指的旋转进行矫正,并将背景灰度值置0;获得ROI,宽度选择为原始图像0.73倍,上边缘截取时选择最下面的边缘坐标,下边缘截取时选择最上面的边缘坐标进行高度截取;对ROI进行直方图均衡化、Gabor滤波;对总计3816张636类静脉图像提取ROI后进行保存;匹配时使用one vs n的方法,使用零均值归一化的方向计算两张ROI图像的相似度分数,范围为0到1,数值越接近1代表相似程度越高,与待匹配图像相似程度最高的那张图像所属主体即判断为待匹配图像主体。
The invention discloses a finger vein recognition method and system, comprising: determining the upper and lower edge point sets of the finger area, and thinning the edge to a pixel width; Edge point set; according to the obtained pixel point coordinates, correct the rotation of the finger, and set the background gray value to 0; obtain the ROI, select the width as 0.73 times the original image, select the bottom edge coordinates when intercepting the upper edge, and select the lower edge When intercepting, select the uppermost edge coordinates for height interception; perform histogram equalization and Gabor filtering on the ROI; extract ROIs from a total of 3816 636 types of vein images and save them; use the one vs n method for matching, and use zero-mean normalization Calculate the similarity score of two ROI images in the direction of normalization, ranging from 0 to 1. The closer the value is to 1, the higher the similarity. The subject of the image with the highest similarity to the image to be matched is judged as the subject of the image to be matched. .
Description
技术领域technical field
本发明涉及图像处理以及生物特征识别技术领域,具体涉及一种手指静脉识别方法及系统。The invention relates to the technical fields of image processing and biological feature recognition, in particular to a finger vein recognition method and system.
背景技术Background technique
指静脉识别是生物特征识别技术的一种,指静脉识别技术是依据血液流动可以吸收特点波长关系的特性,使用近红外光线照射手指,可以拍摄到侵袭的指静脉图像。由于指静脉特征难以被复制,并且每个人的指静脉特征都不相同,同时随年龄增长几乎不会发生变化,因此指静脉识别技术具有活体识别、安全性高、唯一性等特点,在公司门禁、酒店管理、政府机构、监狱门禁、医学鉴定等方面有着巨大的应用前景。Finger vein recognition is a kind of biometric recognition technology. Finger vein recognition technology is based on the characteristics of blood flow that can absorb characteristic wavelength relationships. Using near-infrared light to irradiate fingers, images of invading finger veins can be captured. Because the characteristics of finger veins are difficult to be copied, and the characteristics of each person's finger veins are different, and they will hardly change with age. Therefore, finger vein recognition technology has the characteristics of living body recognition, high security, and uniqueness. , Hotel management, government agencies, prison access control, medical appraisal, etc. have great application prospects.
在指静脉识别或者验证过程中,采集静脉图像时由于光照不稳定,手指的旋转可能导致采集的图像质量参差不齐,所以需要一种对于光照以及手指旋转带来的误差比较鲁棒的算法使得指静脉识别能够在实际生活中应用。In the process of finger vein recognition or verification, due to unstable illumination when collecting vein images, the rotation of fingers may lead to uneven image quality. Therefore, an algorithm that is more robust to errors caused by illumination and finger rotation is needed. Finger vein recognition can be applied in real life.
发明内容Contents of the invention
本发明要解决的技术问题在于,针对目前指静脉识别算法的不足,提供了一种手指静脉识别方法及系统来解决上述问题。The technical problem to be solved by the present invention is to provide a finger vein recognition method and system to solve the above problems in view of the shortcomings of current finger vein recognition algorithms.
一种手指静脉识别方法,包括:A finger vein recognition method, comprising:
S1、在原始图像中确定手指区域的上下边缘点集,细化边缘到一个像素宽度,所述原始图像中的手指区域水平放置;S1. Determine the upper and lower edge point sets of the finger area in the original image, refine the edge to a pixel width, and place the finger area in the original image horizontally;
S2、从细化后的边缘点集中选取合适的点进行边缘扩展,获得真实边缘点集;S2. Select suitable points from the thinned edge point set to perform edge extension to obtain a real edge point set;
S3、对手指旋转进行矫正,并根据真实边缘点集,将非手指区域灰度值置0;S3. Correct the finger rotation, and set the gray value of the non-finger area to 0 according to the real edge point set;
S4、对经过S3处理的图像进行裁剪,宽度选择为原始图像的0.73±5%倍,优选为0.73 倍,上边缘截取时选择最下面的边缘坐标、下边缘截取时选择最上面的边缘坐标进行高度截取,得到静脉感兴趣区域ROI;S4, the image processed through S3 is cropped, and the width selection is 0.73 ± 5% times of the original image, preferably 0.73 times, and the bottom edge coordinates are selected when the upper edge is intercepted, and the uppermost edge coordinates are selected when the lower edge is intercepted. Highly intercepted to obtain the ROI of the vein region of interest;
S5、对静脉感兴趣区域ROI进行直方图均衡化和Gabor滤波,得到图像增强后的静脉感兴趣区域ROI,待匹配使用;S5. Perform histogram equalization and Gabor filtering on the ROI of the vein ROI to obtain the ROI of the vein ROI after image enhancement, to be used for matching;
S6、对预设的多个类别的多张静脉图像使用S1-S5的步骤处理提取静脉感兴趣区域ROI 并进行保存;S6. Using the steps of S1-S5 to process and extract vein ROIs for multiple preset categories of vein images and save them;
S7、静脉识别匹配采用one vs n的方法,使用零均值归一化的方法计算每两张静脉感兴趣区域ROI图像的相似度分数,根据相似度分数识别出属于同一类别的静脉感兴趣区域 ROI图像。S7. The one vs n method is adopted for vein identification and matching, and the similarity score of each two vein ROI images is calculated using the zero-mean normalization method, and the vein ROI belonging to the same category is identified according to the similarity scores image.
进一步的,步骤S1中,具体包括:Further, in step S1, specifically include:
S11、获得边缘点集a,当前像素点灰度值与其上方距离2个坐标的像素点灰度值相差超过33则认定该像素点为边缘点;S11. Obtain the edge point set a, if the difference between the gray value of the current pixel point and the gray value of the pixel point 2 coordinates above it is more than 33, then the pixel point is determined to be an edge point;
S12、获得边缘点集b,使用Sobel算子计算整幅图像的梯度,当前像素点梯度超过梯度方向相邻两个像素梯度值时则认定该像素点为边缘点;S12. Obtain the edge point set b, and use the Sobel operator to calculate the gradient of the entire image. When the current pixel gradient exceeds the gradient value of two adjacent pixels in the gradient direction, the pixel is determined to be an edge point;
S13、对边缘点集a和边缘点集b做交集运算,获得所需的边缘点集,但是只保留横坐标频数前15的像素点,对边缘进行细化,即每个纵坐标下至多只有一个上边缘点和一个下边缘点;S13. Perform an intersection operation on the edge point set a and the edge point set b to obtain the required edge point set, but only keep the first 15 pixels in the frequency of the abscissa, and refine the edge, that is, there are at most an upper edge point and a lower edge point;
S14、边缘点集在图像中表示为厚度不均匀的横向曲线,对其进行细化处理,将边缘细化至一个像素宽度。S14. The edge point set is represented in the image as a horizontal curve with uneven thickness, and thinning is performed on it, and the edge is thinned to a width of one pixel.
进一步的,步骤S2中,在上下边缘点集中分别选择一个纵坐标最接近中心的点作为起始点扩展边缘,设该点(x,y),向该点左边扩展时若相邻的三个坐标(x-1,y-1),(x-1,y),(x-1,y+1)有一个灰度值为255,则把这个相邻点设置为边缘点,继续向左扩展,若没有一个灰度值为255,则取这三个坐标在S1中计算出的梯度最大的点作为边缘点,通过不断地扩展获得完整手指轮廓。Further, in step S2, in the set of upper and lower edge points, respectively select a point whose ordinate is closest to the center as the starting point to expand the edge, set the point (x, y), if the three adjacent coordinates are extended to the left of the point (x-1, y-1), (x-1, y), (x-1, y+1) has a gray value of 255, then set this adjacent point as an edge point and continue to expand to the left , if none of the grayscale values is 255, then take the point with the largest gradient calculated in S1 of these three coordinates as the edge point, and obtain the complete finger outline through continuous expansion.
进一步的,步骤S3中,在0.23宽度和0.77宽度处选择四个边缘点(x1,y1),(x2,y1),(x3,y2),(x4,y2),计算手指旋转的角度:根据计算出的旋转角度将图像旋转至水平,并把背景灰度值置0。Further, in step S3, select four edge points (x1, y1), (x2, y1), (x3, y2), (x4, y2) at the width of 0.23 and 0.77, and calculate the angle of finger rotation: Rotate the image to the horizontal according to the calculated rotation angle, and set the background gray value to 0.
进一步的,步骤S4中,具体包括:Further, in step S4, specifically include:
S41、使用宽度为50的矩形窗,从静脉的中间坐标开始往右移动,每移动一个坐标计算窗口灰度平均值,返回灰度平均值最大的5个窗口坐标,从这5个中选择坐标最小的作为纵坐标基线,往左截取原始图像0.73倍宽度的静脉感兴趣区域ROI;S41. Use a rectangular window with a width of 50, move to the right from the middle coordinate of the vein, calculate the average gray value of the window every time you move a coordinate, and return the 5 window coordinates with the largest average gray value, and select the coordinates from these 5 The smallest one is used as the baseline of the ordinate, and the vein region of interest ROI of 0.73 times the width of the original image is intercepted to the left;
S42、宽度截取完成后进行高度截取,图像上边缘点集选择最下面的边缘坐标,下边缘点集选择最上面的边缘坐标进行高度截取,获得静脉感兴趣区域ROI。S42. After the width interception is completed, perform height interception, select the bottom edge coordinates for the upper edge point set of the image, and select the uppermost edge coordinates for the lower edge point set to perform height interception to obtain the ROI of the vein ROI.
进一步的,步骤S5中,具体包括:Further, in step S5, specifically include:
S51、对截取得到的ROI进行直方图均衡化,具体地,使用一种限制对比度自适应直方图均衡化算法;S51. Perform histogram equalization on the intercepted ROI, specifically, use a limited contrast adaptive histogram equalization algorithm;
S52、对直方图均衡化后的ROI进行0°,45°,90°,135°的Gabor四方向滤波。S52. Perform Gabor four-direction filtering of 0°, 45°, 90°, and 135° on the ROI after the histogram equalization.
进一步的,步骤S7中,具体包括:Further, in step S7, specifically include:
S71、首先对所有静脉图像进行类别的编码;S71. Firstly, classify all vein images;
S72、选取一张作为待匹配图像,将待匹配图像与候选图像分成8个矩形区域,计算每对位置对应的矩形区域的ZNCC值,计算两张图像的平均ZNCC值作为相似度衡量值, ZNCC的计算需要两张图片的平方差之和,各自的灰度平均值,标准差,设两个对应矩形区域的尺寸为(2n+1)×(2n+1),中心坐标分别为(u1,v1),(u2,v2),平方差之和为灰度平均值为:标准差为: ZNCC为: 取8个矩形区域的平均ZNCC值作为相似度度量标准,ZNCC取值范围为0到1,数值越接近1代表相似程度越高,与待匹配图像相似程度最高的那张图像所属类即判断为待匹配图像所属类;S72. Select one as the image to be matched, divide the image to be matched and the candidate image into 8 rectangular areas, calculate the ZNCC value of the rectangular area corresponding to each pair of positions, and calculate the average ZNCC value of the two images as the similarity measurement value, ZNCC The calculation requires the sum of the square differences of the two images, their respective grayscale averages, and standard deviations. Let the size of the two corresponding rectangular areas be (2n+1)×(2n+1), and the center coordinates are (u 1 ,v 1 ),(u 2 ,v 2 ), the sum of square differences is The average gray value is: The standard deviation is: ZNCC is: The average ZNCC value of 8 rectangular areas is taken as the similarity measure standard. The ZNCC value ranges from 0 to 1. The closer the value is to 1, the higher the similarity is. The class of the image with the highest similarity to the image to be matched is judged as The category of the image to be matched;
S73、计算待匹配图像与数据库内剩余图像的相似度并排序,选择相似度最大的图像所在类作为预测类,与实际类进行比较,若相等,则预测正确,反之预测错误;S73. Calculate and sort the similarity between the image to be matched and the remaining images in the database, select the class of the image with the largest similarity as the predicted class, and compare it with the actual class. If they are equal, the prediction is correct, otherwise the prediction is wrong;
S74:匹配时预测正确的次数/匹配总次数即为算法的准确率。S74: The number of correct predictions during matching/total number of matches is the accuracy rate of the algorithm.
一种手指静脉识别系统,包括:处理器及存储设备;所述处理器加载并执行所述存储设备中的指令及数据用于实现权利要求1~7所述的任意一种手指静脉识别方法。A finger vein recognition system, comprising: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement any one of the finger vein recognition methods described in claims 1-7.
本发明的有益效果在于:本手指静脉识别方法采用边缘扩展的方式,能够十分准确地提取到静脉图案的ROI,同时对图像采集过程中手指可能产生的旋转进行了矫正,采用了一种直方图均衡化方法,使得图像在匹配过程中对光照更加鲁棒,采用了四方向的Gabor滤波器,增强了静脉的纹理特征,使用ZNCC这一图匹配的方法,在山东大学公开的手指静脉数据库上达到了98.7%的准确率。The beneficial effect of the present invention is that: the finger vein recognition method adopts the method of edge expansion, can extract the ROI of the vein pattern very accurately, and at the same time corrects the possible rotation of the finger during the image acquisition process, and adopts a histogram The equalization method makes the image more robust to the light during the matching process. The four-directional Gabor filter is used to enhance the texture characteristics of the veins. Using the ZNCC image matching method, the finger vein database released by Shandong University is used. An accuracy rate of 98.7% was achieved.
附图说明Description of drawings
下面将结合附图及实施例对本发明作进一步说明,附图中:The present invention will be further described below in conjunction with accompanying drawing and embodiment, in the accompanying drawing:
图1为本发明手指静脉识别方法的ROI提取流程图;Fig. 1 is the ROI extraction flowchart of finger vein recognition method of the present invention;
图2为本发明手指静脉识别方法的认证匹配流程图;Fig. 2 is the authentication matching flowchart of finger vein recognition method of the present invention;
图3为本发明手指静脉识别方法的ROI提取时静脉图像的变化过程;Fig. 3 is the changing process of the vein image when the ROI of the finger vein recognition method of the present invention is extracted;
图4为本发明手指静脉识别方法的等错误率曲线;Fig. 4 is the equal error rate curve of the finger vein recognition method of the present invention;
图5为本发明手指静脉识别方法的准确率曲线。Fig. 5 is the accuracy rate curve of the finger vein recognition method of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚,下面将结合附图对本发明实施方式作进一步地描述。In order to make the purpose, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be further described below in conjunction with the accompanying drawings.
如图1、图2所示,一种手指静脉识别方法,包括如下步骤:As shown in Figure 1 and Figure 2, a finger vein recognition method comprises the following steps:
S1、在原始图像中确定手指区域的上下边缘点集,细化边缘到一个像素宽度,所述原始图像中的手指区域水平放置;S1. Determine the upper and lower edge point sets of the finger area in the original image, refine the edge to a pixel width, and place the finger area in the original image horizontally;
具体过程如下:The specific process is as follows:
S11、获得边缘点集a,当前像素点灰度值与其上方距离2个像素点的灰度值相差超过 33则认定该像素点为边缘点。S11. Obtain the edge point set a. If the difference between the gray value of the current pixel point and the gray value of the two pixels above it exceeds 33, the pixel point is determined to be an edge point.
S12、获得边缘点集b,使用Sobel算子计算整幅图像的梯度,当前像素点梯度超过梯度方向相邻两个像素梯度值时则认定该像素点为边缘点。S12. Obtain the edge point set b, and use the Sobel operator to calculate the gradient of the entire image. When the gradient of the current pixel exceeds the gradient values of two adjacent pixels in the gradient direction, the pixel is determined as an edge point.
S13、对边缘点集a和边缘点集b做交集运算,获得所需的边缘点集,但是只保留横坐标频数前15的像素点,对边缘进行细化,即每个纵坐标下至多只有一个上边缘点和一个下边缘点。S13. Perform an intersection operation on the edge point set a and the edge point set b to obtain the required edge point set, but only keep the first 15 pixels in the frequency of the abscissa, and refine the edge, that is, there are at most One upper edge point and one lower edge point.
S14、边缘点集在图像中表示为厚度不均匀的横向曲线,对其进行细化处理,将边缘细化至一个像素宽度。S14. The edge point set is represented in the image as a horizontal curve with uneven thickness, and thinning is performed on it, and the edge is thinned to a width of one pixel.
S2、从细化后的边缘点集中选取合适的点进行边缘扩展,获得真实边缘点集;S2. Select suitable points from the thinned edge point set to perform edge extension to obtain a real edge point set;
具体过程如下:The specific process is as follows:
在上下边缘点集中分别选择一个纵坐标最接近中心的点作为起始点扩展边缘,设该点 (x,y),向该点左边扩展时若相邻的三个坐标(x-1,y-1),(x-1,y),(x-1,y+1)有一个灰度值为255,则把这个相邻点设置为边缘点,继续向左扩展,若没有一个灰度值为255,则取这三个坐标在S1中计算出的梯度最大的点作为边缘点,通过不断地扩展获得完整手指轮廓。In the set of upper and lower edge points, select a point whose ordinate is closest to the center as the starting point to expand the edge. Let this point (x, y) expand to the left of the point if the adjacent three coordinates (x-1, y- 1), (x-1, y), (x-1, y+1) has a gray value of 255, then set this adjacent point as an edge point, and continue to expand to the left, if there is no gray value is 255, the point with the largest gradient calculated in S1 by these three coordinates is taken as the edge point, and the complete finger outline is obtained by continuous expansion.
S3、为减少采集图像时手指旋转导致匹配时准确率下降的可能性,对手指旋转进行矫正,并根据真实边缘点集,将非手指区域灰度值置0;S3. In order to reduce the possibility of the finger rotation leading to a drop in accuracy during image acquisition, the finger rotation is corrected, and the gray value of the non-finger area is set to 0 according to the real edge point set;
具体过程如下:The specific process is as follows:
在0.23宽度和0.77宽度处选择四个边缘点(x1,y1),(x2,y1),(x3,y2),(x4, y2),计算手指旋转的角度:根据计算出的旋转角度将图像旋转至水平。并把背景灰度值置0。Select four edge points (x1, y1), (x2, y1), (x3, y2), (x4, y2) at 0.23 width and 0.77 width, and calculate the angle of finger rotation: Rotates the image horizontally according to the calculated rotation angle. And set the background gray value to 0.
S4、对经过S3处理的图像进行裁剪,宽度选择为原始图像的0.73±5%倍,优选为0.73 倍,上边缘截取时选择最下面的边缘坐标、下边缘截取时选择最上面的边缘坐标进行高度截取,得到静脉感兴趣区域ROI;S4, the image processed through S3 is cropped, and the width selection is 0.73 ± 5% times of the original image, preferably 0.73 times, and the bottom edge coordinates are selected when the upper edge is intercepted, and the uppermost edge coordinates are selected when the lower edge is intercepted. Highly intercepted to obtain the ROI of the vein region of interest;
具体过程如下:The specific process is as follows:
S41、使用宽度为50的矩形窗,从静脉的中间坐标开始往右移动,每移动一个坐标计算窗口灰度平均值,返回灰度平均值最大的5个窗口坐标,从这5个中选择坐标最小的作为纵坐标基线,往左截取原始图像0.73倍宽度的ROI。S41. Use a rectangular window with a width of 50, move to the right from the middle coordinate of the vein, calculate the average gray value of the window every time you move a coordinate, and return the 5 window coordinates with the largest average gray value, and select the coordinates from these 5 The smallest one is used as the baseline of the ordinate, and the ROI of 0.73 times the width of the original image is intercepted to the left.
S42、宽度截取完成后进行高度截取,图像上边缘点集选择最下面的边缘坐标,下边缘点集选择最上面的边缘坐标进行高度截取,获得静脉感兴趣区域(ROI)。S42. Perform height interception after the width interception is completed, select the bottom edge coordinates for the upper edge point set of the image, and select the uppermost edge coordinates for the lower edge point set to perform height interception to obtain a vein region of interest (ROI).
S5、对静脉感兴趣区域ROI进行直方图均衡化和Gabor滤波,得到图像增强后的静脉感兴趣区域ROI,待匹配使用;S5. Perform histogram equalization and Gabor filtering on the ROI of the vein ROI to obtain the ROI of the vein ROI after image enhancement, to be used for matching;
具体过程如下:The specific process is as follows:
S51、对截取得到的ROI进行直方图均衡化,具体地,使用一种限制对比度自适应直方图均衡化算法(CLAHE)。S51. Perform histogram equalization on the intercepted ROI, specifically, use a limited contrast adaptive histogram equalization algorithm (CLAHE).
S52、对直方图均衡化后的ROI进行0°,45°,90°,135°的Gabor四方向滤波。S52. Perform Gabor four-direction filtering of 0°, 45°, 90°, and 135° on the ROI after the histogram equalization.
S6、对总计3816张636类静脉图像提取ROI后进行保存;S6. Save the ROI after extracting a total of 3816 vein images of 636 categories;
S7、S7、匹配时使用one vs n的方法,使用零均值归一化(Zero Mean NormalizedCross- Correlation,ZNCC)的方向计算两张ROI图像的相似度分数,范围为0到1,数值越接近 1代表相似程度越高,与待匹配图像相似程度最高的那张图像所属主体即判断为待匹配图像主体。S7, S7, using the method of one vs n when matching, using the direction of Zero Mean Normalized Cross-Correlation (ZNCC) to calculate the similarity score of the two ROI images, ranging from 0 to 1, the closer the value is to 1 The higher the degree of similarity is, the subject of the image with the highest similarity to the image to be matched is judged as the subject of the image to be matched.
具体过程如下:The specific process is as follows:
S71、首先对所有静脉图像进行类别的编码。S71. Firstly, classify all vein images.
S72、选取一张作为待匹配图像,将待匹配图像与候选图像分成8个矩形区域,计算每对位置对应的矩形区域的ZNCC值,计算两张图像的平均ZNCC值作为相似度衡量值,ZNCC的计算需要两张图片的平方差之和,各自的灰度平均值,标准差,设两个对应矩形区域的尺寸为(2n+1)×(2n+1),中心坐标分别为(u1,v1),(u2,v2),平方差之和为灰度平均值为:标准差为: ZNCC为: 取8个矩形区域的平均ZNCC值作为相似度度量标准,ZNCC取值范围为0到1,数值越接近1代表相似程度越高,与待匹配图像相似程度最高的那张图像所属类即判断为待匹配图像所属类。S72. Select one as the image to be matched, divide the image to be matched and the candidate image into 8 rectangular areas, calculate the ZNCC value of the rectangular area corresponding to each pair of positions, and calculate the average ZNCC value of the two images as the similarity measure value, ZNCC The calculation requires the sum of the square differences of the two images, their respective grayscale averages, and standard deviations. Let the size of the two corresponding rectangular areas be (2n+1)×(2n+1), and the center coordinates are (u 1 ,v 1 ),(u 2 ,v 2 ), the sum of square differences is The average gray value is: The standard deviation is: ZNCC is: Take the average ZNCC value of 8 rectangular areas as the similarity measure standard. The ZNCC value ranges from 0 to 1. The closer the value is to 1, the higher the similarity is. The class of the image with the highest similarity to the image to be matched is judged as The category of the image to be matched.
S73、计算待匹配图像与数据库内剩余图像的相似度并排序,选择相似度最大的图像所在类作为预测类,与实际类进行比较,若相等,则预测正确,反之预测错误。S73. Calculate and sort the similarity between the image to be matched and the remaining images in the database, select the class of the image with the highest similarity as the predicted class, and compare it with the actual class. If they are equal, the prediction is correct, otherwise the prediction is wrong.
S74:匹配时预测正确的次数/匹配总次数即为算法的准确率。S74: The number of correct predictions during matching/total number of matches is the accuracy rate of the algorithm.
本发明的ROI提取的图像变换过程在图3中表示,3.1代表原图,3.2代表提取出的点集a,3.3代表提取出的点集b,3.4代表点集a和点集b的交集,3.5代表扩展的边缘,3.6 代表将背景灰度值置0的静脉图像,3.7代表矫正手指旋转后的静脉图像,3.8代表直方图均衡化的ROI图像,3.9代表在3.8基础上使用四方向Gabor滤波器的ROI图像。The image transformation process that ROI of the present invention extracts is represented in Fig. 3, and 3.1 represents the original image, and 3.2 represents the point set a extracted, and 3.3 represents the point set b extracted, and 3.4 represents the intersection of point set a and point set b, 3.5 represents the extended edge, 3.6 represents the vein image with the background gray value set to 0, 3.7 represents the vein image after correcting finger rotation, 3.8 represents the ROI image with histogram equalization, and 3.9 represents the use of four-directional Gabor filtering on the basis of 3.8 Imager ROI image.
如图4所示,横坐标代表认假率,纵坐标代表拒真率,随着设置的相似度阈值变化而变化,预测正确但是相似度小于阈值时计算入拒真率,预测错误但相似度大于阈值时计算入认假率,右下角的20*40,40*80,80*160代表ROI缩放后的分辨率,虚线与实线交点代表不同ROI分辨率下的等错误率,可以看到ROI缩放到40*80时效果最好。As shown in Figure 4, the abscissa represents the false recognition rate, and the ordinate represents the false rejection rate, which changes with the set similarity threshold. When the prediction is correct but the similarity is less than the threshold, the false rejection rate is calculated, and the prediction is wrong but the similarity is false. When it is greater than the threshold, the false recognition rate is calculated. The 20*40, 40*80, and 80*160 in the lower right corner represent the resolution of the ROI after zooming in. The intersection of the dotted line and the solid line represents the equal error rate under different ROI resolutions. You can see It works best when the ROI is scaled to 40*80.
如图5所示,横坐标代表设置的相似度阈值,纵坐标代表准确率,可以看到ROI分辨率在40*80时,相似度阈值设置为0.68时准确率最高。As shown in Figure 5, the abscissa represents the set similarity threshold, and the ordinate represents the accuracy rate. It can be seen that when the ROI resolution is 40*80, the accuracy rate is the highest when the similarity threshold is set to 0.68.
本发明提出的一种手指静脉识别方法及系统,针对目前指静脉识别算法的不足,采用了根据手指轮廓的ROI提取方式,同时增加了手指的旋转矫正操作,增强了图像采集时对手指偏移旋转的鲁棒性,采用了CLAHE直方图均衡化,将不同光照下图像的对比度进行拉伸,增强了对光照条件不同的鲁棒性,采用了四方向的Gabor滤波器,增强了静脉纹路,采用ZNCC的图匹配方法,同时把ROI分成八个部分,分别求取相似度并取平均值。本发明在山东大学公开的手指静脉数据库上进行了验证,得到98.7%左右的准确率,由于该数据库的静脉图像质量较低,因此在静脉图像采集设备较好的情况下,可以到达更高的准确率,在身份认证识别方面可以得到广泛应用。A finger vein recognition method and system proposed by the present invention, aimed at the shortcomings of the current finger vein recognition algorithm, adopts the ROI extraction method based on the finger contour, and at the same time increases the rotation correction operation of the finger, and enhances the finger deviation during image acquisition. The robustness of the rotation adopts the CLAHE histogram equalization to stretch the contrast of the image under different lighting conditions, which enhances the robustness to different lighting conditions. The four-directional Gabor filter is used to enhance the vein lines. Using the graph matching method of ZNCC, the ROI is divided into eight parts at the same time, and the similarity and average value are calculated respectively. The present invention is verified on the finger vein database disclosed by Shandong University, and the accuracy rate is about 98.7%. Since the vein image quality of this database is low, it can reach a higher accuracy under the condition of better vein image acquisition equipment. The accuracy rate can be widely used in identity authentication and recognition.
上面结合附图对本发明的实施例进行了描述,但是本发明并不局限于上述的具体实施方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在本发明的启示下,在不脱离本发明宗旨和权利要求所保护的范围情况下,还可做出很多形式,这些均属于本发明的保护之内。Embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementations, and the above-mentioned specific implementations are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, and these all belong to the protection of the present invention.
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