期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2013
卷号：6
期号：3
出版社：SERSC
摘要：In this paper, we propose a novel and effective image model—Image Latent Semantic Analysis (ILSA) for extracting latent semantic features of face image, and recognizing face with Support Vector Machine (SVM). The novel feature extraction by the ILSA model can be better overcome the impact of some negative factors, such as the image quality fuzzy, illumination changes effect. The main contribution of the paper is that the ILSA features can obtain a wealth of information than the conventional image semantic features and has a stronger expression and classification abilities than the low-level features. The experimental results on the ORL and large-scale FERET databases show that the proposed algorithm significantly outperforms other well-known algorithms.
