利用小波变换提高基于KPCA方法的人脸识别性能

利用小波变换提高基于KPCA方法的人脸识别性能

杨绍华¹, 林盘²,潘晨¹

1. 宁夏大学数学计算机学院, 宁夏银川 750021;2. 福建师范大学软件学院, 福建福州 350007

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-10-24 发布日期:2006-10-24
通讯作者: 杨绍华

Performance improvement of face recognition based on kernel principal component analysis using wavelet transform

YANG Shao-hua¹,LIN Pan²,PAN Chen¹

1． School of Mathematics and Computer Science, Ningxia University, Yinchuan 750021;2. Software College, Fujian Normal University, Fuzhou 350007, Fujian

Received:1900-01-01 Revised:1900-01-01 Online:2006-10-24 Published:2006-10-24
Contact: YANG Shao-hua

摘要/Abstract

摘要： 基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能. 然而并非所有非线性特征对识别都有利，过多的不相关特征可能会降低识别性能. 针对图像信息冗余的特点，预先对图像进行小波变换，通过消除对识别无关的细节信息，不仅提高了KPCA方法的识别精度，而且降低了该算法对计算机硬件的要求. 同时，为了抑制KPCA对光照等变化的较高敏感性，还提出一种对图像灰度进行衰减的预处理策略. 基于ORL数据库的实验表明，综合上述措施的系统比传统方法具有更快的训练速度和更高的识别精度.

关键词: 人脸识别, 小波变换, 核主成分分析

Abstract: The algorithm of face recognition based on kernel principal component analysis（KPCA）can abstract nonlinear features of image and can get better performance under less sample training conditions. Not all nonlinear features are beneficial to the recognition. The superabundant unrelated features may reduce the recognition performance. The image was transformed by wavelet transformation for its redundancy, which not only has improved the accuracy of recognition but has reduced the demand for computer hardware of the algorithm. A pretreatment strategy that can reduce image gradation was developed in order to restrain upper sensitivity of KPCA to the change of illumination. The experimental results based on ORL-DATABASE show that the above-mentioned algorithm allows faster training speed and higher accuracy of recognition than traditional ones.

Key words: kernel principal component analysis , wavelet transform, face recognition

中图分类号:

TP391

杨绍华,林盘,潘晨 . 利用小波变换提高基于KPCA方法的人脸识别性能[J]. J4, 2007, 42(9): 96-100 .

YANG Shao-hua,LIN Pan,PAN Chen . Performance improvement of face recognition based on kernel principal component analysis using wavelet transform[J]. J4, 2007, 42(9): 96-100 .

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