Hybrid framework for face recognition with expression & illumination variations

In this paper, a hybrid framework is proposed to improve the performance of face recognition by combining global descriptors and local appearance descriptors and proved that their complementary nature makes them good candidates in the better recognition of faces. The proposed face recognition method can handle facial appearance variations which are caused by facial expression and illumination under controlled capture conditions. Different from traditional face recognition methods, the proposed method uses multiple features which are extracted using Global and Local feature extraction algorithms like Principal Component Analysis (PCA) & Local Binary Pattern (LBP). Wavelet fused feature vector has richer information than feature vector extracted using unifeature extraction algorithms. Radial Basis Function (RBF) is used to classify feature vectors. The proposed method has been extensively evaluated on the standard benchmark databases like ORL and Grimace. It is found that significant results obtained in comparison with well-known generic face recognition methods.