关键词: 人脸识别, 环形对称Gabor变换（CSGT）, 主成分分析（PCA）, 独立成分分析（ICA）, 最近邻分类器

Abstract: This paper presents a new feature extraction method for face recognition using 2D Circularly Symmetrical Gabor Transform（2DCSGT） and Independent Component Analysis（ICA）.A circularly symmetrical Gabor feature vector is derived from a set of downsampled circularly symmetrical Gabor wavelet representations of face images.The dimension of the circularly symmetrical Gabor feature vector is reduced by means of Principal Component Analysis（PCA）.Independent Circularly Symmetrical Gabor Features（ICSGF） are defined based on Independent Component Analysis.To show the validity of the proposed method，it is applied to face recognition on the ORL，YALE and FERET databases.In particular，the ICSGF method achieves 99.5% correct face recognition accuracy for ORL database，93.33% accuracy for Yale database and 97.14% accuracy for FERET database.Experimental results show that the algorithm is feasible and effective for face recognition.

Key words: face recognition, Circularly Symmetrical Gabor Transform（CSGT）, Principal Component Analysis（PCA）, Independent Component Analysis（ICA）, nearest neighbor classifier