Face recognition using composite classifier with 2DPCA

Jia Li; Ding Yan

doi:10.1117/12.2265516

23 January 2017 Face recognition using composite classifier with 2DPCA

Jia Li, Ding Yan

Author Affiliations +

Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 103221P (2017) https://doi.org/10.1117/12.2265516
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China

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.

Citation Download Citation

Jia Li and Ding Yan " Face recognition using composite classifier with 2DPCA", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103221P (23 January 2017); https://doi.org/10.1117/12.2265516

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