Exploit more information of the sample for representation based face recognition
by Kezong Tang; Xuan Xiao; Zuoyong Li
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 8, No. 4, 2015

Abstract: Besides the representation based classification method defined in the original space for face recognition, the method has also been extended to the feature space. As defined in the original space the method uses a linear combination of all the training samples to approximate the test sample in the original space and then exploits the determined linear combination of all the training samples of each class to classify the test sample. As defined in the feature space the method first implicitly transforms the sample into a high-dimensional space, then classify the test sample in the feature space. In this paper, we propose to integrate the original data of the sample and its representation in the feature space for face recognition based on their respective rationales. The proposed method outperforms the representation based classification method defined in the original space.

Online publication date: Mon, 03-Aug-2015

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 Wireless and Mobile Computing (IJWMC):
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