NDLI: Adaptive Weberfaces for occlusion-robust face representation and recognition

Content Provider	IET Digital Library
Author	Li, Xiao Xin He, Lin Hao, Pengyi Liu, Zhiyong Li, Jingjing
Abstract	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.
Starting Page	964
Ending Page	975
Page Count	12
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 11, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0365
Journal	IET Image Processing
Publisher Date	2017-07-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Weberfaces AR Benchmark Database Computer Vision And Image Processing Technique Extended Yale B Benchmark Database Face Recognition Feature Extraction Feature Locality Feature Redundancy Human Visual Perception Change Model Image Dimension Image Recognition Image Representation Kernel Locality Kernel Orientation LFW Benchmark Database MSMO AdapWeber MSMO WeberFace Multiscale-and-multiorientation AdapWeber Occlusion Level Occlusion-robust Face Recognition Occlusion-robust Face Representation Occlusion-robust Feature Occlusion-weak Feature Single-scale-and-single-orientation AdapWeber SSSO AdapWeber UMB-DB Benchmark Database Visual Perception Weber Ratio Weber's Law
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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