Face recognition based on multi-module singular value features and probabilistic subspaces analysis

摘要：

The paper introduces a face recognition method using probabilistic subspaces analysis on multi-module singular value features of face images. Singular value vector of a face image is valid feature for identification. But the recognition rate is low when only one module singular value vector is used for face recognition. To improve the recognition rate, many sub-images are obtained when the face image is divided in different ways, with all singular values of each image used as a new sample vector of the face image. These multi-module singular value vectors include all features of a face image from local to the whole, so more discriminant information for face recognition is obtained. Subsequently, probabilistic subspaces analysis is used under these multi-module singular value vectors. The experimental results demonstrate that the method is obviously superior to corresponding algorithms and the recognition rate is respectively 97.5％ and 99.5％ in ORL and CAS-PEAL-R1 human face image databases.

关键词： face recognition probabilistic subspaces analysis multi-module singular value decomposition

作者: Dengyi Chen Lin Cao

作者单位: Department of Telecommunication Engineering Beijing Information Science and Technology University Beijing, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1529-1533

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）