NDLI: Face recognition based on adaptive weighting and fuzzy fusion with single training sample

Content Provider	IEEE Xplore Digital Library
Author	Xiaohua Wang Chao Jin Wei Liu Min Hu Fuji Ren
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. & Inf., Anhui Province Key Lab. of Affective Comput. & Adv. Intell. Machines, Hefei Univ. of Technol., Hefei, China (Xiaohua Wang; Chao Jin; Wei Liu; Min Hu; Fuji Ren)
Abstract	In order to solve the robustness issues for single training sample face recognition under occlusion conditions, this paper presents a recognition method based on adaptive weighting and fuzzy fusion. In our method, the information entropy expansion mode is introduced in sub-mode method and via adaptively assigning the weights corresponding to each sub-model can reduce the impact of occluded region. In addition, through summing the similar blocks in face images can make up for the defect of the sub-model which ignores the integrity of face. Finally, the method based on fuzzy comprehensive evaluation is utilized for decision-level fusion to the outputs by these two ways of classification. Experimental results on AR face database show that this method has state-of-the-art classification accuracy and also robustness to occlusion.
Starting Page	259
Ending Page	264
File Size	746320
Page Count	6
File Format	PDF
ISBN	9781479947201
e-ISBN	9781479947195
DOI	10.1109/CCIS.2014.7175739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Sub-model Fuzzy comprehensive evaluation Adaptive weighting Single training sample Robustness Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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