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

Content Provider	IEEE Xplore Digital Library
Author	Dengyi Chen Lin Cao
Copyright Year	2011
Description	Author affiliation: Department of Telecommunication Engineering, Beijing Information Science and Technology University, China (Dengyi Chen; Lin Cao)
Abstract	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.
Starting Page	1508
Ending Page	1512
File Size	542721
Page Count	5
File Format	PDF
ISBN	9781424493043
e-ISBN	9781424493067
DOI	10.1109/CISP.2011.6100445
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training probabilistic subspaces analysis Image recognition Databases Face recognition multi-module face recognition Probabilistic logic Vectors Face singular value decomposition
Content Type	Text
Resource Type	Article

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