Comparison of three face recognition algorithms

Author

Zhang, Chaoyang ; Zhou, Zhaoxian ; Sun, Hua ; Dong, Fan

Author_Institution

Sch. of Comput., Univ. of Southern Mississippi, Hattiesburg, MS, USA

fYear

2012

fDate

19-20 May 2012

Firstpage

1896

Lastpage

1900

Abstract

Face recognition has received a lot of attention in biometrics and computer vision. A lot of face recognition algorithms have been developed during the past decades. This paper reviews three classical methods Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Elastic Bunch Graph Matching (EBGM). Three algorithms are implemented with Matlab. The algorithm performance is evaluated on three different databases. Scenarios and performance benchmarking are compared for each of the algorithms in terms of recognition accuracy, computational cost, and recognition tolerance.

Keywords

computer vision; face recognition; graph theory; performance evaluation; principal component analysis; EBGM; LDA; Matlab; PCA; algorithm performance evaluation; biometrics; computational cost; computer vision; elastic bunch graph matching; face recognition algorithm; linear discriminant analysis; performance benchmarking; principal component analysis; recognition accuracy; recognition tolerance; Accuracy; Databases; Face; Face recognition; Principal component analysis; Training; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems and Informatics (ICSAI), 2012 International Conference on

Conference_Location

Yantai

Print_ISBN

978-1-4673-0198-5

Type

conf

DOI

10.1109/ICSAI.2012.6223418

Filename

6223418