Infrared face recognition based on LBP co-occurrence matrix and partial least squares Online publication date: Sun, 04-Jan-2015
by Zhihua Xie; Guodong Liu
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 8, No. 1, 2015
Abstract: Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. This paper proposes an infrared face recognition method based on Local Binary Pattern (LBP) co-occurrence matrix. In traditional LBP-based features such as LBP histogram, space locations information, which is an important feature for recognition, is discarded. Considering such spatial relations in infrared faces, we introduce co-occurrence matrix of LBP codes, instead of histogram, to obtain more discriminative representation for location features. To cope with the high dimensions of LBP co-occurrence matrix, the final classifier formulates Partial Least Squares (PLS) regression for accurate classification. The experimental results show combination of LBP co-occurrence matrix and PLS achieve better infrared face recognition performance compared to state-of-the-art approaches.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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:
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
Our Blog 
Follow us on Twitter 
Visit us on Facebook