Discriminative discriminant common vector in face verification

Pang, Ying Han and Liew, Yee Ping and Goh, Fang Ling and Teoh, Andrew Ben Jin and Loo, Chu Kiong (2014) Discriminative discriminant common vector in face verification. In: 2014 International Conference on Computer and Information Sciences (ICCOINS). IEEE, pp. 1-5. ISBN 978-1-4799-4391-3

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Abstract

Discriminant Common Vectors (DCV) is proposed to solve small sample size problem. Face recognition encounters this dilemma where number of training samples is always smaller than the data dimension. In literature, it is shown that DCV is efficient in face recognition. In this paper, DCV is enhanced for further boosting its discriminating power. This modified version is namely Discriminative Discriminant Common Vectors (DDCV). In this technique, a local Laplacian matrix of face data is computed. This matrix is used to derive a regularization model for computing discriminative class common vectors. Experimental results demonstrate that DDCV illustrates its effectiveness on face verification, especially on facial images with significant intra class variations.

Item Type: Book Section
Uncontrolled Keywords: Regularization; Discriminant Common Vector; feature extraction; face; verification
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Information Science and Technology (FIST)
Centre for Diploma Programmes (CDP)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 05 Mar 2015 02:51
Last Modified: 05 Jan 2017 04:25
URI: http://shdl.mmu.edu.my/id/eprint/5995

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