关键词: 人脸识别, 二维主成分分析, 类内自适应加权平均值

Abstract: Modular 2DPCA method based on within-class weight average face recognition is presented.Firstly， the within-class weight average of each sub-image of all training samples in each class is calculated，and it is used to normalize corresponding sub-images of the within-class samples.After that，the optimal projecting matrix from general matrix that is made up of all normalized sub-images can be obtained accordingly.Secondly，when all sub-images of training samples and testing samples are projected to the optimal projecting matrix that has been gotten above，the recognition features are produced.Finally，the nearest distance classification is used to distinguish each face.The experiment results on ORL face database indicate that the recognition performance of the proposed method is obviously superior to that of 2DPCA and general modular 2DPCA.

Key words: face recognition, two-dimensional principal component analysis, within-class weight average