Comparative study: face recognition on unspecific persons using linear subspace methods

Author

Lin, Dahua ; Yan, Shuicheng ; Tang, Xiaoou

Author_Institution

Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, China

Volume

3

fYear

2005

fDate

11-14 Sept. 2005

Abstract

Recently many automatic face recognition (AFR) systems were developed for applications with unspecific persons, which is different from conventional pattern recognition problems where all classes are known in the training stage. In this paper, we present a systematic and comprehensive study on linear subspace methods for face recognition on unspecific persons. Over 6700 experiments using different algorithms with different training parameters and testing conditions are conducted on a large scale database (4550 samples) to investigate the compound effect of various influential factors. The observations based on these experiments are expected to provide widely applicable guidelines for designing practical AFR systems.

Keywords

face recognition; principal component analysis; automatic face recognition systems; large scale database; linear subspace methods; pattern recognition problems; training parameters; unspecific persons; Face recognition; Guidelines; Large-scale systems; Linear discriminant analysis; Pattern recognition; Performance analysis; Principal component analysis; Scattering; Spatial databases; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2005. ICIP 2005. IEEE International Conference on

Print_ISBN

0-7803-9134-9

Type

conf

DOI

10.1109/ICIP.2005.1530504

Filename

1530504