A Novel Technique for Human Face Recognition Using Nonlinear Curvelet Feature Subspace

Abstract

This paper proposes a novel human face recognition system using curvelet transform and Kernel based principal component analysis. Traditionally multiresolution analysis tools namely wavelets and curvelets have been used in the past for extracting and analyzing still images for recognition and classification tasks. Curvelet transform has gained significant popularity over wavelet based techniques due to its improved directional and edge representation capability. In the past features extracted from curvelet subbands were dimensionally reduced using principal component analysis for obtaining an enhanced representative feature set. In this work we propose to use an improved scheme using kernel based principal component analysis (KPCA) for a comprehensive feature set generation. KPCA performs a nonlinear principal component analysis (PCA) using an integral kernel operator function and obtains features that are more meaningful than the ones extracted using a linear PCA. Extensive experiments were performed on a comprehensive database of face images and superior performance of KPCA based human face recognition in comparison with state-of-the-art recognition is established.