Null space-based kernel Fisher discriminant analysis for face recognition

The space-based LDA takes full advantage of the space while the other methods remove the space. It proves to be optimal in performance. From the theoretical analysis, we present the NLDA algorithm and the most suitable situation for NLDA. Our method is simpler than all other space approaches, it saves the computational cost and maintains the performance simultaneously. Furthermore, kernel technique is incorporated into our space method. Firstly, all samples are mapped to the kernel space through an efficient kernel function, called cosine kernel, which have been demonstrated to increase the discriminating capability of the original polynomial kernel function. Secondly, a truncated NLDA is employed. The novel approachh only requires one eigenvalu analysis and is also applicable to the large sample size problem. Experiments are carried out on different face data sets to demonstrate the effectiveness of the proposed method.