Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

FDLDA: An Fast Direct LDA Algorithm For Face Recognition

Authors
Zhibo Guo, Kejun Lin, Yunyang Yan
Corresponding Author
Zhibo Guo
Available Online August 2016.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Science and Electronic Technology
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.78How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.78
DO  - https://doi.org/10.2991/cset-16.2016.78
ID  - Guo2016/08
ER  -