NDLI: Imerging local feature and fuzzy weighting for 2DPCA face recognition

Content Provider	IEEE Xplore Digital Library
Author	Liu Lina Shi Chengdong
Copyright Year	2011
Description	Author affiliation: School of Electrify and Electronic, Shandong University of Technology, Zibo, China (Liu Lina; Shi Chengdong)
Abstract	Two Dimensional Principal Component Analysis (2DPCA) extracts the global feature of human face, but the local feature is very important to face recognition. In this paper, a method of imerging local feature and fuzzy weighting for 2DPCA face recognition was proposed. Firstly, two local feature areas — Upper area and Tzone area were divided, 2DPCA analysis is made to the view picture image and two regions independently. After the initial classify results were fuzzy weighted, minimum distance classification was used for face recognition, the purpose of unifying local feature and overall feature is carried on, the status of eye, nose, mouth and so on local features in the recognition is prominent. The experiments on ORL face databases demonstrate the proposed method's effectiveness and feasibility.
Starting Page	5295
Ending Page	5298
File Size	427876
Page Count	4
File Format	PDF
ISBN	9781424481620
e-ISBN	9781424481651
DOI	10.1109/ICECENG.2011.6057202
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzuy weighting Face recognition Humans Educational institutions Feature extraction Local feature Face Two dimensional principal component analysis (2DPCA) Principal component analysis
Content Type	Text
Resource Type	Article

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