A novel combined fisherfaces framework

A novel method for face recognition (FR) based on a combination of global features extracted by nonlinear kernel principal component analysis and local features derived by applying Gabor wavelets is discussed. It is well known that the distribution of face images is highly nonlinear under a large variation in viewpoints. Therefore, linear methods such as principle component analysis (PCA) or linear discriminant analysis (LDA) cannot provide reliable and robust solutions for FR problems. In our framework, the proposed LDA in the unitary space makes use of the null space of the within-class scatter matrix effectively, and complex vectors integrate global feature vectors and local feature vectors as input feature of the proposed LDA. The experiment results demonstrate that the proposed methodology is more effective and robust for face recognition with complex face variations.