Orthogonal isometric projection for face recognition

作     者：LU Guan-ming ZUO Jia-kuo 

作者机构：College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2011年第18卷第1期

页      面：91-97,128页

核心收录：

学科分类：081603[工学-地图制图学与地理信息工程] 07[理学] 08[工学] 080203[工学-机械设计及理论] 070503[理学-地图学与地理信息系统] 0705[理学-地理学] 0816[工学-测绘科学与技术] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China (61071167) the Natural Science Foundation of Jiangsu Province (BK2008075),and the Natural Science Foundation for Colleges and Universities in Jiangsu Province (08KJB510016) 

主　　题：lsoProjection O-IsoProjection face recognition dimensionality reduction 

摘      要：Isometric projection （IsoProjection） is a linear dimensionality reduction method, which explicitly takes into account the manifold structure embedded in the data. However, IsoProjection is non-orthogonal, which makes it extremely sensitive to the dimensions of reduced space and difficult to estimate the intrinsic dimensionality. The non-ortbogonality also distorts the metric structure embedded in the data. This paper proposes a new method called orthogonal isometric projection （O-IsoProjection）, which shares the same linear character as IsoProjection and overcomes the metric distortion problem of IsoProjection. Similar to IsoProjection, O-lsoProjection firstly constructs an adjacency graph which can reflect the manifold structure embedded in the data and the class relationship between the sample points of face space, and then obtains the projections by preserving such a graph structure. Different from IsoProjection, O-IsoProjection requires the basis vectors to be orthogonal, and the orthogonal basis vectors can be calculated by iterative way. Experimental results on ORL and Yale databases show that O-lsoProjection has better recognition rate for face recognition than Eigenface, Fisherface and IsoProjection.