Algorithm Research of Face Image Gender Classification Based on 2-D Gabor Wavelet Transform and SVM

Gender classification is one of the most challenging problems in the field of pattern recognition. The pixel-based gray image recognition method is quite sensitive to illumination variation and has high dimensions for computation. PCA-based image feature recognition algorithm can reduce the image dimension, but it is only on the basis of optimal entropy to choose face features which neglects the different gender information between the male and female. In order to overcome the disturbance of non-essential information such as illumination variations and facial expression changing, a new algorithm is proposed in this paper. That is, the 2-D Gabor transform is used for extracting the face features; a new method is put forwards to decrease dimensions of Gabor transform output for speeding up SVM training; finally gender recognition is accomplished with SVM classifier. Good performance of gender classification test is achieved on a relative large scale and low-resolution face database.