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1、基于误差扩散的指纹匹配算法11郝瑛谭铁牛王蕴红中国科学院自动化所模式识别国家重点实验室北京100080摘要：匹配模块是指纹身份鉴别系统中的核心模块。在采用细节特征描述指纹的系统中，指纹匹配问题就 转化为点模式匹配。关键字：误差扩散、指纹匹配、Hough变换1概述给定两个指纹特征模板（参考模板和输入模板），匹配过程通常给出两个指纹模板相似 程度的度量。同时，匹配还要设定一个门限，用来确定两个模板是否是从同一个手指提取的。 在点模式匹配的理想情况下2算法2.1特征点的相似度度量在介绍特征的相似性度量之前，首先介绍一下指纹特征的表示。在特征提取过程中， 为了去除虚假特征点，我们在细化图像上对每个检测
2、出的端点和分叉点所在的纹路进行跟 踪。跟踪的结束条件有两个：跟踪长度达到预先设定的某个值或者遇到另外一个特征点。2.1.1对应点估计对应点估计的目的是要找到一对或者若干对最为可靠的对应点。我们首先根据上一节定义的 相似度度量，找出所有匹配上的端点对和分叉点对，然后使用Hough变换的方法找出最可 靠的匹配点对作为对应点。下面将对具体的算法进行描述。图5.两幅指纹图像的匹配结果，相似度：0. 739表1中列出了相同手指的不同样木以及不同手指匹配值的均值。1.才以及相同手指和不同手指匹配值的均值和方差数据库d均值（相同 手指）方差（相同 手指）均值（不同 手指）方差（不同 手指）NIST-246.
3、5688.3214.935.659.76参考文献11 Anil K. Jain, Lin Hong, Sharath Pankanti, Ruud Bolle, An Identity-Authentication System Using Fingerprint, Proc. of the IEEE, Vol. 85, No.9, 1997.2 A. Ranade, A. Rosenfeld, “Point Pattern Matching by Relaxation”，Pattern Recognition, Vol. 26, No. 2, pp.269-276, 1993.3 D. Sk
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10、3D Face Verification from Range DataGang Pan and Zhaohui WuInstitute of Computer System EngineeringZJU-Miaxis Joint Lab of Embeded and Biometrics TechnologyZhejiang Universisty, Hangzhou 310027, P.R.Chinagpan, wzh Abstract In this paper, we presented an automatic approach for 3D face verification fr
11、om rane data. The method consists of range data registration and 3D face comparison. There are two steps in registration procedure. The coarse step conducts the normalization by exploiting a priori knowledge of the human face and facial features.Keywords: 3D face verification. Range data1 Introducti
12、onThe automatic face recognition based on 2D image processing has been actively researched in recent years, and many techniques have been presented. Although great strides have been made during the past three decades, the task of robust face recognition is still difficult.2 3D face databaseOur exper
13、imental results use the 3D face data from 3D_RMA database in M2VTS project5. The range data are obtained by a 3D acquisition system based on structured light.Figure I: Two sample models in xyz form from the manual DBFace data registrationA 3D face recognition system generally makes up of two key par
14、ts: 3D data registration and comparison. The accuracy of the registration will greatly impact on the result of following comparison.2.1.1 The coarse normalizationBefore the fine registration step, the coarse alignment is performed. We assumes that the given range data are 3D facial models.Table 1: C
15、omparison of equal error ratesBcumicrf5Oursautomatic DB session 17.25%6.67%automatic DB session 27.75%6.67%automatic DB session 1-29.0%7.33%manual DB session 14.75%3.24%References1 G G Gordon, “Face Recognition Based on Depth Maps and Surface Curvature/9 SPIE Proceedings. Vol. 1570: Geometric Methods in Computer Vision, pp. 234-247, 1991.2 G G Gordon, Face Recognition Based on Depth and Curvature Features/ Proc. IEEE CVPR92. pp. 808-810June 1992.J.C. Lee, E. Milios, Matching Range Images of Human Faces, Proc. ICCV90, pp. 722-726, 1990.
