Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

Ziqiang Wang, Xia Sun, Lijun Sun, Yuchun Huang
2013 The Scientific World Journal  
Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper. SKMFA can make use of both labelled and unlabeled samples to learn the projection matrix for nonlinear dimensionality reduction. Meanwhile, it can successfully avoid the singularity problem by not
more » ... culating the matrix inverse. In addition, in order to make the nonlinear structure captured by the data-dependent kernel consistent with the intrinsic manifold structure, a manifold adaptive nonparameter kernel is incorporated into the learning process of SKMFA. Experimental results on three face image databases demonstrate the effectiveness of our proposed algorithm.
doi:10.1155/2013/981840 pmid:24163638 pmcid:PMC3791838 fatcat:fpynhn4ohnhtbcoqhu3frv45bu