关键词: 人脸识别, 特征融合, 离散余弦变换（DCT）, 局部二值模式（ELBP）

Abstract: It is not enough that capture various facial information using only one descriptor to face features extraction. Though, DCT（Discrete Cosine Transform） can extract the frequency feature of face image, it ignores the relationships between the adjacent pixels and abandoned the texture information. ELBP（Elongated Local Binary Pattern） consideres the direction in local area and the texture information but without global information. In this paper, a novel method is proposed that addressed the problems by fusing DCT and ELBP. The centralized DCT coefficients are used as frequency feature, and local features of the mouth and eyes area are extracted by ELBP. Then, fusing the two features by PCA to get more effective features. Finally, 1-NN classifier is chosen to evaluate the proposed feature. Experiments on ORL and Yale face database show that the proposed method is better than just single DCT, ELBP method or the fusion method of LBP and DCT, and also improves the accuracy of face recognition.

Key words: face recognition, feature fusion, Discrete Cosine Transform（DCT）, Elongated Local Binary Pattern（ELBP）