中文摘要：

提出一种基于Gabor相位特征的多通道组合模型人脸识别方法。该算法对各方向上的Gabor相位特征采用双向二维主元分析进行维数约简,然后组合各方向上约简后的特征而构建最终人脸模板。算法主要包括4个步骤：（1）通过Gabor滤波器组与人脸原始灰度图像的卷积来提取不同尺度和不同方向的人脸相位特征;（2）双向二维主成分分析对人脸各方向上的Gabor相位特征进行维数约简;（3）组合各方向上约简后的特征矩阵得到一个增强型的特征矩阵,量化该矩阵得到最终的二元人脸模板;（4）采用基于海明距离的最近邻分类器进行分类。ORL和Yale人脸数据库上的人脸识别试验表明：该算法是一种有效的人脸识别算法,而且理论分析表明计算量相对而言也明显减少。

英文摘要：

Gabor phase feature-based multiple channel assembling algorithm is presented for face recognition,in which Gabor phase features of face in different scales and orientations are firstly extracted and then combine these features for matching.Four main steps are involved in the proposed algorithm：（i） Gabor phase features of different scales and in different directions are extracted by the convolution of Gabor filter banks and original gray face images;（ii） two-directional two-dimensional principal component analysis is made to conduct dimensionality reduction of Gabor phase features from all directions;（iii） assembling the reduced feature matrixes from all directions,an enhanced feature matrix is formed,and quantizing the enhanced feature matrix,the final binarry face template is achieved;（iv） the Hamming metric based nearest neighbor classifier is used for classification.The results of face recognition experiments by the ORL and Yale face databases show the effectiveness of the proposed method and theoretical analysis proves its role of lowering the amount of computation.