Share Email Print
cover

Proceedings Paper

Gabor feature based class-dependence feature analysis for face recognition
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we introduce a novel Gabor based Spacial Domain Class-Dependence Feature Analysis(GSD-CFA) method that increases the Face Recognition Grand Challenge (FRGC)2.0 performance. In short, we integrate Gabor image representation and spacial domain Class-Dependence Feature Analysis(CFA) method to perform fast and robust face recognition. In this paper, we mainly concentrate on the performances of subspace-based methods using Gabor feature. As all the experiments in this study is based on large scale face recognition problems, such as FRGC, we do not compare the algorithms addressing small sample number problem. We study the generalization ability of GSD-CFA on THFaceID data set. As FRGC2.0 Experiment #4 is a benchmark test for face recognition algorithms, we compare the performance of GSD-CFA with other famous subspace-based algorithms in this test.

Paper Details

Date Published: 8 February 2010
PDF: 9 pages
Proc. SPIE 7532, Image Processing: Algorithms and Systems VIII, 75320M (8 February 2010); doi: 10.1117/12.840070
Show Author Affiliations
Zhongkai Han, Tsinghua Univ. (China)
Chi Fang, Tsinghua Univ. (China)
Xiaoqing Ding, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 7532:
Image Processing: Algorithms and Systems VIII
Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

© SPIE. Terms of Use
Back to Top