Human face gender identification system based on MB-LBP

Face gender recognition is an important research field in computer vision and pattern recognition. Face gender recognition is the use of computer technology to analyze face images and extract effective face features, so as to realize the recognition of gender attributes of observation objects. In this paper, we used Multi-Block Local Binary Pattern to extract gender features, and used different support vector machine learning models to process and analyze the results. The experiments show that the combination of MB-LBP algorithm and Linear SVM is better than the combination of MB-LBP algorithm and and RBF. The experimental results show that the recognition capability of MB-LBP+SVM based on the FERET database is higher than that of the SVM, KNN+SVM and PCA+LDA+ SVM.