关键词: 人脸性别分类，独立分量分析，遗传算法，支持向量机

Abstract: The gender of a face is almost its most salient feature, and realizing automatic gender classification according to the face image will boost the performance of face retrieval and face recognition in large face database. This paper proposed a new gender classification method combining independent analysis (ICA) and genetic algorithm(GA). The Fast ICA algorithm was used to derive independent basic images and projection vectors out of the face images and each image was represented as a feature vector projected in the low-dimensional space spanned by the basis vectors. Then,a genetic algorithm was used to select a subset of features which scan to encode important information about gender form the low-dimensional representation. Finally, the SVM classifier was trained to perform gender classification using the selected independent features subset, The local features extraction of ICA, the features selection ability of UA, and the strong classify ability of SVM were combined reasonably. The experiment results show that the method gets a better classifier performance.

Key words: Face gender classification, Independent component analysis, Uenetic algorithm, Support vector machine