计算机工程 ›› 2010, Vol. 36 ›› Issue (10): 16-18.doi: 10.3969/j.issn.1000-3428.2010.10.005

• 博士论文 • 上一篇    下一篇

基于独立分量分析的二级指纹分类算法

项 明,吴小培,刘明生   

  1. (安徽大学计算智能与信号处理教育部重点实验室,合肥 230039)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-05-20 发布日期:2010-05-20

Secondary Fingerprint Classification Algorithm Based on Independent Component Analysis

XIANG Ming, WU Xiao-pei, LIU Ming-sheng   

  1. (Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-05-20 Published:2010-05-20

摘要: 针对传统指纹分类算法分类不均衡的缺陷,提出一种基于独立分量分析的二级指纹分类算法。从高阶统计相关性角度出发提取一组特征指纹图像,以该组图像为基,利用该组图像构成的特征空间将指纹图像线性表出,结合系数向量和Henry分类模式将指纹库细分为11个子类,建立二级索引。应用结果表明,该算法可节省运算时间,降低复杂度。

关键词: 指纹分类, 中心点, 三角点, 独立分量分析

Abstract: Aiming at the shortage of classification unbalanced in traditional fingerprint classification algorithm, this paper presents a secondary fingerprint classification algorithm based on independent component analysis. It extracts a group of characteristic fingerprint image in terms of high level statistics relevance, uses this group of characteristic as base images, the fingerprint can be projected into the feature space. Combining coefficient vector with Henry classification mode to set up two level index which classifies input fingerprints into eleven kinds of category. Application results show that this algorithm can save operation time and reduce complexity.

Key words: fingerprint classification, core point, delta point, Independent Component Analysis(ICA)

中图分类号: