Face Recognition under Varying Illumination Using Green’s Functionbased Bidimensional Empirical Mode Decomposition and Gradientfaces

School of electronics and information engineering, Chongqing Three Gorges University, 404000, wanzhou, Chongqing, China

Abstract

A novel face recognition approach under varying illumination condition based on Green’s function in tension-based bid imensional empirical mode decomposition (GiT-BEMD) and gradient faces (GBEMDGF) is present. Firstly, face image was illumination normalization by discrete cosine transform that an appropriate number of DCT coefficients are truncated in logarithm domain. And then, two intrinsic mode functions (IMFs) that relevant of intrinsic physical significances of face images are produced by Gi T-BEMD. At the same time, gradient faces is used to improve the high frequency component of face images and to extract illumination insensitive facial feature. The facial feature of discriminately are fused using IMFs and illumination insensitive feature. Secondly, the principal component analysis is adopted to reduce the dimension of face image. The nearest neighbourhood classifier based on cosine distance is implemented for face classification. Experimental results on Yale B database and CUM PIE face database demonstrate that the present technique is robust to varying lighting resource.