Study On Gender Recognition Using Canonicalcor Relation Analysis

Gender recognition is involved in pattern recognition, psychology, artificial intelligence,computer vision, it has a certain degree of applications in human-machine dialogue, image and video retrieval, demographic information collection, security access system, identity authentication,etc,therefore it has received widely attentions. However, gender recognition is still in an exploratory stage,and each method has its limitations. How to improve automaticity and accuracy of gender identification is still a problem worthy of studying. Because the method that use canonical correlation analysis(CCA) to fuse two groups of features of one pattern has shows great superiority in face recognition, handwriting recognition and expression recognition,the application of CCA in gender recognition is focused on in this paper. The main contribution is as follows:1. Some widely used methods for feature extraction such as local binary pattern(LBP),histogram of oriented gradient(HOG), C1 features and the methods for classification including support vector machine(SVM), AdaBoost and random forest(RF) are described. Experiments on gender recognition using these methods are conducted on MIT and VIPeR databases of pedestrian images, different features and classification methods are compared. The experimental results show that C1 features is better than LBP and HOG features, and using SVM can obtain better performance in gender recognition.2. An algorithm for gender recognition from body images based on canonical correlation analysis(CCA) and SVM is presented. First, LBP, HOG and C1 features are pairwise fused using CCA method to obtain three kinds for fused features including LBP-HOG,LBP-C1 and HOG-C1 feature, then the fusion features are inputted into SVM for gender recognition. The three kinds of original features and three kinds of fused features are compared by experiments on MIT and VIPeR database. The experimental results show that fusion feature by CCA is better than single feature,moreover,HOG-C1 feature works best.3. The influence of orientation on gender recognition is considered,and a new algorithm for gender recognition from body image based on CCA and the orientation classification is presented.First,HOG and C1 features are fused using CCA method,then SVM is used for orientation classification. According to the distance from test sample to the SVM hyperplane, the orientation of images are grouped into three classes, i.e., front view,mixed view and back view. Finally, one classifier trained for the view of the test image is used for gender recognition. The experimental results show that the proposed method makes further improvement in recognition rate of gender recognition.