关键词: 人脸识别, 类增广PCA, 小波变换, 特征提取

Abstract: A face recognition approach based on wavelet transform and adaptive class-augmented PCA is proposed.Human face images are compressed using discrete wavelet transform.The features of low-frequency component are extracted by the adaptive class-augmented PCA.Different from traditional class-augmented PCA，the method does not require constructing the between-class information，so it is used more flexibly.Because of the wavelet transform，the recognition rate and time-consuming are both improved.Experiment on Yale and FERET shows the effectiveness of the algorithm.

Key words: face recognition, Class-Augmented Principal Component Analysis（CAPCA）, wavelet transform, feature extraction