Manifold adaptive kernel local fisher discriminant analysis for face recognition

作者：Wang Ziqiang^*; Sun Xia

来源：Journal of Multimedia, 2012, 7(6): 387-393.

DOI：10.4304/jmm.7.6.387-393

• Adaptive kernels
• Face database
• Face images
• Fisher discriminant analysis
• High dimensionality
• Kernel function
• Local manifold structure
• Manifold learning
• Nearest neighbors
• Non-linear variation

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.

• 出版日期2012
• 单位河南大学