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Selecting discriminant eigenfaces for face recognition
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AI
DOI：10.1016/j.patrec.2004.11.029.png)
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In realistic race recognition applications, such its surveillance photo identification. supervised learning algorithms usually rail when only one training sample per subject is available. The lack of training samples and the considerable image variations due to aging, illumination and pose variations. make recognition it challenging task, This letter proposes a development of the traditional eigenface solution by applying it feature selection process on the extracted eigenfaces. The proposal calls for the establishment of it feature subspace in which the intrasubject variation is minimized while the intersubject variation is maximized. Extensive experimentation following the FERET evaluation protocol suggests that in the scenario considered here, the proposed scheme improves significantly the recognition performance of the eigenface solution and outperforms other state-of-the-art methods. (c) 2004 Elsevier B.V. All rights reserved.
Keyword:
face recognition
eigenface selection
intersubject variation
intrasubject variation
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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