Two-dimensional exponential discriminant analysis for small sample size in face recognition
by Nitin Kumar; R.K. Agrawal
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 5, No. 3, 2016

Abstract: Appearance-based face recognition methods usually require converting 2D face images to 1D column vectors. Due to high dimensionality and few available samples, the performance becomes unsatisfactory. In this paper, we propose a novel technique called two-dimensional exponential discriminant analysis (2DEDA) which extracts important features for classification directly from 2D face images. 2DEDA is especially suitable when the available sample size is small. Experimental results on two publicly available datasets viz. AR and CMU-PIE demonstrate the efficacy of the proposed technique, outperforming two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA). The performance of the algorithms is evaluated in terms of average classification accuracy.

Online publication date: Mon, 22-Aug-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Artificial Intelligence and Soft Computing (IJAISC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com