Sparse representation based gender recognition algorithm

Abstract

Gender recognition is using the feature extracted from the training sets to identify the gender of a test sample. To achieve this propose, we should grip the difference between the features of male and female. Here, Gabor and LBP are used to get features of face image. The relationship between the feature of training sets and test samples is much more than distance or projection. By analyzing the mathematic essence of sparse representation between images, a test sample can be viewed as the linear combination of training samples and based on this theory, the sparse representation algorithm of gender recognition is proposed. Finally, some experiments on gender recognition verified the efficacy of the proposed algorithm. © 2013 Springer-Verlag.