期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 38, 期 4, 页码 3246-3251出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.09.011
关键词
Face recognition; Third-order tensor; Higher order singular value decomposition; Common matrix approach
In the classical common matrix approach (CMA), the common matrix for each individual face class is obtained using basis matrices calculated by Gram-Schmidt orthogonalization of the class covariance matrix. This common matrix represents the common or invariant properties of a given face class. The CMA idea relies on the concept of basis matrices of a face class which span the, so called, difference sub-space of that class. In this paper, an alternative method to obtain the basis matrices for CMA is proposed. The basis matrices are obtained using the higher order singular value decomposition (HOSVD) of a third-order tensor constructed with face images and these basis matrices are utilized in the construction of CMA. In order to exemplify the improvements in the recognition rates, face recognition experiments are carried out via the AR face database. The original face matrices, as well as 2DPCA-, 2DSVD-, and 2DFDA-based feature matrices are applied as the input matrices for the two different implementations of CMA in the experimental studies. The results indicate that the recognition rates obtained by the proposed method are slightly higher than those obtained using the basis matrices calculated by Gram-Schmidt orthogonalization. (C) 2010 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据