摘要
特征提取是在自动指纹识别系统中重要的一步 为了有效地利用大量的指纹弯曲信息对指纹进行描述 ,提出了一种新算法 该算法通过综合邻近几条指纹脊线的信息 ,求出了一种能反映指纹宏观弯曲规律 ,并对单条指纹曲线的不规则不敏感的特征 随后提出了一种基于这些特征的指纹分类方法 实验结果表明 ,该算法提取出的特征清楚地描述了指纹脊线的弯曲规律 ,同时对噪声不敏感 ,可作为指纹识别的辅助特征
In an automatic fingerprint identification system (AFIS), feature extraction is a critical step For effectively using the curve information to describe fingerprint, a novel algorithm is proposed; it embraces information of few fingerprint ridges nearby to extract a new characteristic which can describe the curvature feature of fingerprint Furthermore a new classification method based on macroscopic characteristics is proposed. Experimental results demonstrate that the algorithm is feasible, and the characteristics extracted by it can clearly show the inner macroscopic curve properties of the fingerprint image The result also shows that this kind of characteristic is robust to noise and pollution, e g those from additive Gaussian noise, and can be applied in fingerprint verification as a supplement to the customary minutiae The searching space in matching process is greatly reduced with the aid of the new classification method
出处
《计算机研究与发展》
EI
CSCD
北大核心
2003年第3期453-458,共6页
Journal of Computer Research and Development
关键词
指纹识别
特征
特征提取
曲率
分类
fingerprint recognition
minutiae
feature extraction
curvature
classification
