Face Gender Recognition Research Based on Local Features and Support Vector Machine
In this paper, we proposed a face gender recognition method based on local features and SVM. First, we divide the face image into five parts which are used to instead of the whole face for better recognition performance. Second, we use CS to extract local features of these five parts. Then, we respectively train five single SVM classifiers to achieve one to one feature recognition for local features. Finally, decision information fusion is used to achieve the final classification. Because SVM were successfully used to solve numerous pattern recognition problems and is mainly used to solve two-classification problem, selecting SVM to do gender recognition in our method has the obvious superiority. After a lot of experiments, results show that the proposed method in this paper is stable and effective, greatly improving the efficiency of face gender recognition.