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GA-ANN based technique for face recognition : PCA features vs average grey level value features

conference contribution
posted on 06.12.2017, 00:00 by Xiaolong Fan, Brijesh Verma
This paper investigates a feature selection and classification technique for face recognition using genetic algorithms and artificial neural networks. The experiments using average grey level value features and PCA features have been conducted on a face image dataset which is extracted from FERET benchmark database. For average grey level value features experiments, different sizes of feature extraction areas and different feature combination sequences are investigated. The effects of aging process on the proposed technique are also investigated. A detailed comparative analysis of experimental results is included in this paper.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

8

End Page

14

Number of Pages

7

Start Date

01/01/2005

ISBN-10

1863657150

Location

Sydney, Australia

Publisher

UTS

Place of Publication

Sydney

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

Australian Joint Conference on Artificial Intelligence;Workshop on Learning Algorithms for Pattern Recognition

Exports

CQUniversity

Exports