Open AccessAdaptive Weberfaces for occlusion‐robust face representation and recognition
Author(s) -
Li XiaoXin,
He Lin,
Hao Pengyi,
Liu Zhiyong,
Li Jingjing
Publication year - 2017
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.2017.0365
Subject(s) - artificial intelligence , locality , robustness (evolution) , computer science , pattern recognition (psychology) , facial recognition system , occlusion , redundancy (engineering) , kernel (algebra) , computer vision , feature extraction , face (sociological concept) , mathematics , medicine , social science , philosophy , biochemistry , linguistics , chemistry , combinatorics , sociology , cardiology , gene , operating system
In order to deal with facial occlusion effectively, the authors propose a powerful but simple face representation method, called adaptive Weberfaces (AdapWeber), based on human visual perception change model and the Weber ratio R implied in Weber's law. Specifically, human perception is naturally highly selective and robust to occlusions, and the Weber ratio R is very important to enhance feature redundancy. As feature redundancy and locality are two guiding principles against facial occlusion, they further develop eight variants of AdapWeber, collectively referred to as single‐scale and single‐orientation (SSSO) AdapWeber, by shrinking the kernel locality and varying the kernel orientation of the original AdapWeber, and integrate them to formulate a multi‐scale and multi‐orientation (MSMO) AdapWeber. A natural by‐product of MSMO AdapWeber is MSMO Weberfaces. Experiments on four benchmark databases, including Extended Yale B, AR, UMB‐DB, and LFW, showed that MSMO AdapWeber/Weberfaces, rather than any variant of SSSO AdapWeber/Weberfaces, outperformed several popular feature extraction approaches in many scenarios, especially when the occlusion level is very high or the image dimension is very low. This result demonstrates that several occlusion‐weak features can be combined together to construct an occlusion‐robust feature.