FDLDA: An Fast Direct LDA Algorithm For Face Recognition

DOI

10.2991/cset-16.2016.78How to use a DOI?

Keywords

Feature extraction, fast direct linear discriminant analysis, face recogniation

Abstract

Feature extraction is one of the hot topics in face recognition. However, many face extraction methods will suffer from the "small sample size" problem, such as Linear Discriminant Analysis (LDA). Direct Linear Discriminant Analysis (DLDA) is an effective method to address this problem. But conventional DLDA algorithm is often computationally expensive and not scalable. In this paper, DLDA is analyzed from a new viewpoint via SVD and an fast and robust method named FDLDA algorithm is proposed. The proposed algorithm achieves high efficiency by introducing the SVD on a small-size matrix, while keeping competitive classification accuracy. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhibo Guo
AU  - Kejun Lin
AU  - Yunyang Yan
PY  - 2016/08
DA  - 2016/08
TI  - FDLDA: An Fast Direct LDA Algorithm For Face Recognition
BT  - Proceedings of the 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 334
EP  - 337
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.78
DO  - 10.2991/cset-16.2016.78
ID  - Guo2016/08
ER  -