Open AccessAdaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination
Author(s) -
Huixian Yang,
Yongyong Cai
Publication year - 2016
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2014.0082
Subject(s) - pattern recognition (psychology) , artificial intelligence , facial recognition system , local binary patterns , computer science , feature vector , binary number , invariant (physics) , entropy (arrow of time) , feature (linguistics) , face (sociological concept) , computer vision , feature extraction , mathematics , histogram , image (mathematics) , social science , linguistics , philosophy , physics , arithmetic , quantum mechanics , sociology , mathematical physics
To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub‐graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state‐of‐the‐art methods.