提出了一種新的人臉特征提取方法，該方法采用DCT對(duì)人臉圖像進(jìn)行降維和去噪，并通過(guò)KDA提取人臉特征。基于該特征，采用NN分類(lèi)器，對(duì)ORL人臉庫(kù)進(jìn)行分類(lèi)識(shí)別，僅用28個(gè)特征平均識(shí)別率就達(dá)到97.3%，“留一法”識(shí)別率為99.5%。仿真結(jié)果表明：該方法有效地濾除了人臉圖像中的高頻干擾信息，明顯增強(qiáng)了特征的辨別能力，同時(shí)顯著地降低了特征維數(shù)和計(jì)算復(fù)雜度.

A novel face feature extraction method is presented in this paper. In this method, the raw face images are denoised by DCT, and dimension reduced features are obtained, then the KDA is performed on the feature vectors to enhance discriminant power. Finally, the NN classifier is selected to perform face classification. The experimental results on ORL face database show that the proposed method achieves an average recognition accuracy of 97.3% using only 28 features and the ‘leave one out’ recognition rate is 99.5%. Moreover, the dicriminant power is enhanced effectively, and the computing complexity and feature dimensions are reduced greatly.