Fusion of LBP and Appearance Manifold Discriminant Analysis for Face Recognition
Feng Hai-liang · Journal of Chinese Computer Systems · 2009
Manifold learning method can discover intrinsic low-dimensional submanifold embedded in the high-dimensional image space,but manifold learning is an unsupervised learning method,the discriminative ability of the low-dimensional feature obtained by the algorithm is often lower than those obtained by the conventional dimensionality reduction methods.Furthermore,manifold learning methods are sensitive to the variation of lighting conditions, pose and expression.To address the two problems,this paper introduces a novel appearance manifold discriminant analysis method for face recognition,it first uses LBP operator to obtain the local features of face image,then fuses prior class-label information and nonlinear submanifold of face images to extract discriminative features from the global features which formed by the local features.This method can not only gains a perfect approximation of face appearance manifold,but also enhances local within-class relations.It also does well on the new samples.Experimental results show that the proposed method can improve face recognition performance effectively.