Local Binary Patterns for Gender Classification

Gender classification using facial features has attracted researchers attention recently. Gender classification using texture features of faces exhibited promising improvement over other facial features. Gender classification finds applications in systems which use gender as one of the parameters. Local Binary Patterns (LBP) are known to have good texture representation properties. Through this paper we present a variant of Local Binary Patterns for gender classification which can discriminate the facial textures efficiently. In this method, we used a new neighborhood shape for obtaining LBP as its representation of texture is superior than traditional LBP. We compute the proposed LBP on each non-overlapping blocks of a face image and a histogram of these LBPs is computed. We used these histograms as facial feature vectors for gender classification as these histograms shown their robustness to compression and uniform intensity variations. The classification task has been achieved by using Support Vector Machine (SVM). We compared our method with existing gender classification methods based on LBP with classifier being the same as SVM. The proposed LBP based descriptor outperforms the traditional LBP based methods and achieved 96.17 percent recognition rate on combined frontal face datasets of FERET and FEI.