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|Title:||Block independent component analysis for face recognition|
|Authors:||Zhang, L |
|Publisher:||IEEE Computer Society|
|Source:||14th International Conference on Image Analysis and Processing : Modena, Italy, September 10-14, 2007 : proceedings, p. 217-222 How to cite?|
|Abstract:||This paper presents a subspace algorithm called block independent component analysis (B-ICA) for face recognition. Unlike the traditional ICA, in which the whole face image is stretched into a vector before calculating the independent components (ICs), B-ICA partitions the facial images into blocks and takes the block as the training vector. Since the dimensionality of the training vector in B-ICA is much smaller than that in traditional ICA, it can reduce the face recognition error caused by the dilemma in ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Experiments on the well-known Yale and AR databases validate that the B-ICA can achieve higher recognition accuracy than ICA and enhanced ICA (EICA).|
|Rights:||© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
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|Appears in Collections:||Conference Paper|
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