Open AccessSingle sample description based on Gabor fusion
Author(s) -
Chen Ting,
Gao Tao,
Zhao Xiangmo
Publication year - 2019
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.2018.6665
Subject(s) - gabor wavelet , artificial intelligence , pattern recognition (psychology) , weighting , computer science , orientation (vector space) , computer vision , histogram , face (sociological concept) , wavelet , sample (material) , facial recognition system , image fusion , fusion , scale (ratio) , feature (linguistics) , wavelet transform , mathematics , image (mathematics) , discrete wavelet transform , geography , philosophy , linguistics , chemistry , chromatography , social science , sociology , geometry , radiology , medicine , cartography
Owing to lack of enough face image and invalidation of many traditional face recognition algorithms, face recognition with single training sample is really a great challenge. To solve the above problem, this study proposes a novel local weighted fusion Gabor (LWFG) algorithm. First, one single sample is segmented into a series of block sub‐images, and then, each of these sub‐images is decomposed into a series of multi‐resolution Gabor wavelets with multi‐orientation and multi‐scale. Second, different orientation Gabor wavelets with the same scale are fused. Next, different scale Gabor wavelets with the same orientation are fused according to the proposed fusion criterion. Third, the fusion Gabor feature histograms are calculated in each of the divided local regions. Meanwhile, every local region's information importance is measured by the proposed local image information content model. Finally, the fusion Gabor wavelet histograms are adaptively weighed by weighting map which calculated from information content model. This study conducted simulation experiments on different face databases under the different conditions including partial occlusion, expression change and illumination variation. The results indicated that the proposed LWFG algorithm is more effective with single training sample.