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）

摘要： 仅使用单一算法提取人脸图像的特征不足以捕捉人脸多方面的信息，为了更好地获取人脸面部特征，针对离散余弦变换（Discrete Cosine Transform，DCT）只能提取人脸面部图像的频域特征，而未考虑近邻像素之间的关系、不能提取纹理特质信息等问题进行了研究，提出一种融合DCT特征和伸长的局部二值模式（Elongated Local Binary Pattern，ELBP）的特征提取方法。该方法首先考虑将人脸图像经DCT变换后的少量低频系数作为人脸的频域特征，然后对人脸图像中贡献相对较大的眼部和嘴部区域进行ELBP特征提取，将该ELBP特征作为人脸的空域特征，并采用PCA方法对所提取的空频域特征进行有效融合，得到更有效的人脸特征，最后用最近邻分类器进行识别。在ORL人脸库和Yale人脸库上的实验结果表明：所提方法比单独采用DCT、ELBP方法或采用DCT和LBP相结合的方法提取的特征更有利于识别，提高了识别的准确性。

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）