提出了一種新的人臉特征提取方法，該方法采用DCT對人臉圖像進行降維和去噪，并通過KDA提取人臉特征。基于該特征，采用NN分類器，對ORL人臉庫進行分類識別，僅用28個特征平均識別率就達到97.3%，“留一法”識別率為99.5%。仿真結果表明：該方法有效地濾除了人臉圖像中的高頻干擾信息，明顯增強了特征的辨別能力，同時顯著地降低了特征維數和計算復雜度.

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.