关键词: 人脸识别, Gabor小波, Gabor幅值和相位特征, 主成分分析（PCA）降维, 最近邻分类

Abstract: Three kinds of features including Gabor amplitude, Gabor phase and combination of amplitude and phase features are extracted, PCA is used to reduce the dimensions, and the nearest neighbor classification rule is used for recognition. Recognition results of the three methods are analyzed and compared on six databases including ORL, Yale, Indian, YaleBE, PIE and the FERET. The results show that Gabor wavelet phase features are robust to light variation, which outperforms other methods on YaleBE and Yale datasets. Gabor wavelet amplitude and phase combined feature is more robust to expression and time changes, which gets higher recognition rate on the fb, dup1, dup2 probe sets of FERET dataset.

Key words: face recognition, Gabor wavelet, Gabor amplitude and phase features, Principal Component Analysis（PCA） dimension reduction, nearest neighbor classification