@inproceedings{66f5fa37126044dea98bea66e1c3b740,
title = "Face recognition using composite classifier with 2DPCA",
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.",
keywords = "Composite classifier, Logistic regression, Principal component analysis, SVM",
author = "Jia Li and Ding Yan",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 7th International Conference on Electronics and Information Engineering, ICEIE 2016 ; Conference date: 17-09-2016 Through 18-09-2016",
year = "2017",
doi = "10.1117/12.2265516",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xiyuan Chen",
booktitle = "Seventh International Conference on Electronics and Information Engineering",
address = "United States",
}