title: Semisupervised kernel marginal Fisher analysis for face recognition.
abstract: 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 calculating 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.
authors.affiliation: School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
authors.forename: Ziqiang
authors.initials: Z
authors.lastname: Wang
authors.forename: Xia
authors.initials: X
authors.lastname: Sun
authors.forename: Lijun
authors.initials: L
authors.lastname: Sun
authors.forename: Yuchun
authors.initials: Y
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005145:Face; D006801:Humans; D010363:Pattern Recognition, Automated