Face recognition with manifold-based kernel discriminant analysis

In this paper, by using of the idea that occurring face data may be generated by sampling a probability distribution that has support on or near a sub-manifold of ambient space, we propose the nonlinear method named MKDA based on neighborhood discriminant projection method, for feature extraction and face recognition in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of face images are represented by nonlinear kernel mapping. Experiments on ORL, UMIST, FERET, YALE and CMU-PIE face databases are performed to test and evaluate the proposed algorithm by using some different methods. Experiments indicate the promising performance of the proposed method.