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Proceedings Paper

Face recognition using composite classifier with 2DPCA
Author(s): Jia Li; Ding Yan
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Paper Abstract

In the conventional face recognition, most researchers focused on enhancing the precision which input data was already the member of database. However, they paid less necessary attention to confirm whether the input data belonged to database. This paper proposed an approach of face recognition using two-dimensional principal component analysis (2DPCA). It designed a novel composite classifier founded by statistical technique. Moreover, this paper utilized the advantages of SVM and Logic Regression in field of classification and therefore made its accuracy improved a lot. To test the performance of the composite classifier, the experiments were implemented on the ORL and the FERET database and the result was shown and evaluated.

Paper Details

Date Published: 23 January 2017
PDF: 5 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103221P (23 January 2017); doi: 10.1117/12.2265516
Show Author Affiliations
Jia Li, Beijing Institute of Technology (China)
Ding Yan, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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