Relative gradient local binary patterns method for face recognition under varying illuminations

HuoRong Ren; XinXin Yan; Yan Zhou; Rui Cui; Jianwei Sun; Yang Liu

doi:10.1117/1.JEI.22.4.043013

8 November 2013 Relative gradient local binary patterns method for face recognition under varying illuminations

HuoRong Ren, XinXin Yan, Yan Zhou, Rui Cui, Jianwei Sun, Yang Liu

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 043013 (November 2013). https://doi.org/10.1117/1.JEI.22.4.043013

Abstract

Local binary patterns (LBPs) are effective facial texture feature descriptors in face recognition. However, the performance of original LBP-based face recognition methods rapidly deteriorates in the condition of nonmonotonic illumination variations. In order to overcome this drawback, we propose a LBP-based face recognition approach, namely relative gradient LBPs (RGLBPs), in which the relative gradient is first applied to the original face images to extract illumination invariant features. Then, an LBP describes textural and structural features for face recognition. Finally, the chi-square dissimilarity measure and the nearest neighbor classifier are used for classification. The experimental results validate that the proposed approach is efficient for the illumination problem in face recognition and also robust to expression and age variations.

Citation Download Citation

HuoRong Ren, XinXin Yan, Yan Zhou, Rui Cui, Jianwei Sun, and Yang Liu "Relative gradient local binary patterns method for face recognition under varying illuminations," Journal of Electronic Imaging 22(4), 043013 (8 November 2013). https://doi.org/10.1117/1.JEI.22.4.043013

Published: 8 November 2013

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available