Adaptive Weberfaces for occlusion‐robust face representation and recognition. Issue 11 (4th October 2017)

• Record Type: Journal Article
• Authors: Li, Xiao‐Xin; He, Lin; Hao, Pengyi; Liu, Zhiyong; Li, Jingjing
• Is Part Of: IET image processing. Volume 11:Issue 11(2017)
• Page Start: 964
• Page End: 975
• Publication Date: 2017-10-04
• Subjects: face recognition -- feature extraction -- image representation -- visual perception; adaptive Weberfaces -- occlusion‐robust face representation -- occlusion‐robust face recognition -- human visual perception change model -- Weber ratio -- Weber's law -- feature redundancy -- feature locality -- single‐scale‐and‐single‐orientation AdapWeber -- SSSO AdapWeber -- kernel locality -- kernel orientation -- multiscale‐and‐multiorientation AdapWeber -- MSMO AdapWeber -- MSMO Weberfaces -- extended Yale B benchmark database -- AR benchmark database -- UMB‐DB benchmark database -- LFW benchmark database -- image dimension -- occlusion level -- occlusion‐weak features -- occlusion‐robust feature; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0365
• Languages: English
• ISSNs: 1751-9659

Abstract

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.