title: Two-dimensional maximum local variation based on image euclidean distance for face recognition.
abstract: Manifold learning concerns the local manifold structure of high dimensional data, and many related algorithms are developed to improve image classification performance. None of them, however, consider both the relationships among pixels in images and the geometrical properties of various images during learning the reduced space. In this paper, we propose a linear approach, called two-dimensional maximum local variation (2DMLV), for face recognition. In 2DMLV, we encode the relationships among pixels in images using the image Euclidean distance instead of conventional Euclidean distance in estimating the variation of values of images, and then incorporate the local variation, which characterizes the diversity of images and discriminating information, into the objective function of dimensionality reduction. Extensive experiments demonstrate the effectiveness of our approach.
authors.affiliation: State Key Laboratory of Integrated Services Networks, Xidian University, Xiàn, China. xd_ste_pr@163.com
authors.forename: Quanxue
authors.initials: Q
authors.lastname: Gao
authors.forename: Feifei
authors.initials: F
authors.lastname: Gao
authors.forename: Hailin
authors.initials: H
authors.lastname: Zhang
authors.forename: Xiu-Juan
authors.initials: XJ
authors.lastname: Hao
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016208:Databases, Factual; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*