Manifold Adaptive Kernel Local Fisher Discriminant Analysis for Face Recognition

To efficiently cope with the high dimensionalities and complex nonlinear variations of face images in face recognition task, a novel manifold adaptive kernel local Fisher discriminant analysis algorithm is proposed in this paper. The core idea of this algorithm is as follows: First, the local manifold structure of the face image is modeled by a nearest neighbor graph. Then, an original input kernel function is deformed with respect to the local manifold structure. Finally, the resulting manifold adaptive kernel function is incorporated into the kernel local Fisher discriminant analysis(LFDA) method, which leads to the manifold adaptive kernel LFDA(MAKL) algorithm for face recognition. Experimental results on three popular face databases show that the proposed algorithm performs much better than other related algorithms.