Local Polynomial Approximation-Local Binary Pattern (LPA-LBP) based Face Classification

In literature of face recognition many methods have been proposed which extract features at multiple scales for robust classification. In this paper, we proposed a novel method which utilizes Local Polynomial Approximation (LPA) techniques to capture the directional information of the face image at different scales. LPA based filters are used to obtain directional faces from the normalized face images at multiple scales. Since face image is spatially varied and classification works better when local descriptors are used, we incorporate Local Binary Pattern (LBP) operator to obtain LPA-LBP maps. Blockwise processing is done on LPA-LBP maps to capture the local regional relation among the pixels. Then, finally, Support Vector Machine (SVM) classifier is learned in LPA-LBP feature space for face classification. The final descriptor contains information extracted from different levels and, thus, results in high classification accuracy of the faces. Experiments done on Yale and ORL datasets demonstrate that the proposed method has higher classification accuracy than previously proposed methods.