Open AccessLocal gradient‐based illumination invariant face recognition using local phase quantisation and multi‐resolution local binary pattern fusion
Author(s) -
Nikan Soodeh,
Ahmadi Majid
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2013.0792
Subject(s) - artificial intelligence , pattern recognition (psychology) , local binary patterns , facial recognition system , invariant (physics) , fusion , phase congruency , entropy (arrow of time) , mutual information , binary number , computer science , mathematics , computer vision , image (mathematics) , histogram , linguistics , philosophy , physics , arithmetic , quantum mechanics , mathematical physics
A local‐based illumination insensitive face recognition algorithm is proposed which is the combination of image normalisation and illumination invariant descriptors. Illumination insensitive representation of image is obtained based on the ratio of gradient amplitude to the original image intensity and partitioned into smaller sub‐blocks. Local phase quantisation and multi‐scale local binary pattern, extract the sub‐regions characteristics. Distance measurements of local nearest neighbour classifiers are fused at the score level to find the best match and decision‐level fusion combines the results of two matching techniques. Entropy, class posterior probability and mutual information are utilised as the weights of fusion components. Simulation results on the YaleB, Extended YaleB, AR, Multi‐PIE and FRGC databases show the improved performance of the proposed algorithm under severe illumination with low computational complexity and no reconstruction or training requirement.