一种基于机器学习的指纹纹路方向计算方法  

Fingerprint Ridge Orientation Estimation Based on Machine Learning

作　　者：祝恩[1] 殷建平[1] 张国敏[1] 胡春风[1] 

机构地区：[1]国防科学技术大学计算机学院,长沙410073

出　　处：《计算机研究与发展》2007年第12期2051-2057,共7页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60603015;60373023)~~

摘　　要：纹路方向是指纹图像的基本特征,而方向计算是指纹识别的基础,特征提取和匹配的过程中都需要用到方向.目前大多数纹路方向计算方法都是基于像素之间的灰度关系的.提出了一种用神经网络学习纹路方向的方法.对于正确的纹路方向,该网络的响应值较大;对于错误的纹路方向,该网络的响应值较小.计算指纹图像的方向场时,对于每个纹路图像块,计算网络在各个方向上的响应值,基于每个图像块在每个方向上的响应值可以计算出整个图像的方向场.该方法比现有方法更能正确地计算指纹图像方向场.Fingerprint recognition is a method for biometric authentication. Fingerprint image consists of interleaving ridges and valleys. Ridge termination and bifurcation, uniformly called minutia, are generally used for fingerprint matching. Automatic fingerprint recognition typically goes through a series of processes, including ridge orientation estimation, segmentation, enhancement, minutiae detection and matching. Ridge orientation is one of the fundamental features of a fingerprint image. And orientation estimation is the basis of fingerprint recognition, since it serves for segmentation, enhancement, minutiae extraction and matching. Most existing orientation estimation methods are based on the characteristic of pixel intensity in a block. In this paper neural network is used to learn the ridge orientation. At the training stage, the correct orientations are fed into the network as positive samples, and the incorrect orientations are fed into the network as negative samples. The trained network has the property of responding to true ridge orientation with a large value and of responding to the false ridge orientation with a small value. When estimating fingerprint ridge orientation, the responded values to each orientation at each image block are used to compute the fingerprint orientation field. The proposed method turns out to be more robust than the existing method.

关 键 词：生物认证 指纹识别 纹路方向 神经网络 低通滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]