Face image classification using appearance and texture features
(0) Face image classification is a central problem in computer vision research and information retrieval area. Most image classification systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. To classify images for versatile applications, an effective algorithm is needed urgently. In this paper, we propose a new texture invariant descriptor to represent global features of an image, and propose a new method which combining local appearance feature with this texture descriptor in face image classification application. Results show the superior performance of these combined method over the hierarchical Bayesian classifier, with a reduction of over 2％ in the error rate on a challenging two class dataset from Caltech dataset in face image classification.
image classification appearance feature texture descriptor hierarchical Bayesian classifier
Li Guo Daisheng Luo Yu Liao Honghua Liao
School of Electronics and Information Engineering Sichuan University Chengdu, China Information Engi School of Electronics and Information Engineering Sichuan University Chengdu, China Information Engineering Department Hubei Institute for Nationalities Enshi, China
国际会议
太原
英文
476-480
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）