Face Recognition Based on SVM and 2DPCA
Computer Vision and Pattern Recognition
2011-10-26 v1
Abstract
The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and AT&T; the results show that the proposed method could improve the classification rates.
Cite
@article{arxiv.1110.5404,
title = {Face Recognition Based on SVM and 2DPCA},
author = {Thai Hoang Le and Len Bui},
journal= {arXiv preprint arXiv:1110.5404},
year = {2011}
}
Comments
10 pages, 7 figures, 2 tables, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 3, September, 2011