计算机工程2011，Vol.37Issue(17)：19-22,28,5.
基于KPCA和FMSD的人脸识别
Face Recognition Based on Kernel Principal Component Analysisand Fuzzy Maximum Scatter Difference
摘要
Abstract
Considering the outer classes and inferior problem in Kernel Maximum Scatter Difference(KMSD) method, a new method of face recognition based on Kernel Principal Component Analysis(KPCA) and Fuzzy Maximum Scatter Difference(FMSD) is developed. The KPCA can be benefit to develop the nonlinear structures features in faces. Selecting the eigenvectors that between-class scatter is greater than within-class scatter after projection as optimal projection axis. Distribution information of samples is represented with fuzzy membership degree in the FMSD. It uses the nearest neighbor classifier for face recognition. Experimental results on ORL and YALE face databases show the KFMSD is better than KMSD method.关键词
人脸识别/核主成分分析/模糊最大散度差/核最大散度差/特征提取Key words
face recognition/ Kernel Principal Component Analysis(KPCA)/ Fuzzy Maximum Scatter Difference(FMSD)/ Kernel Maximum Scatter Difference(KMSD)/ feature extraction分类
信息技术与安全科学引用本文复制引用
曾接贤,田金权,符祥..基于KPCA和FMSD的人脸识别[J].计算机工程,2011,37(17):19-22,28,5.基金项目
国家自然科学基金资助项目(60675022) （60675022）
江西省自然科学基金资助项目(2008GZS0034) （2008GZS0034）
航空科学基金资助项目(20085556017) （20085556017）
