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  1. University of Computer Studies, Yangon
  2. Conferences

Extracting and Classifying for Ear Recognition in Biometrics Knowledge

http://hdl.handle.net/20.500.12678/0000004943
827853fe-0df4-45b3-a653-1b06faa456eb
a69fab03-7c84-4436-b261-708cefe3f72a
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11088.pdf 11088.pdf (311 Kb)
Publication type Article
Upload type Publication
Title
Extracting and Classifying for Ear Recognition in Biometrics Knowledge
en
Publication date 2013-02-26
Authors
Thuzar, Myat
Description
Ear detection is an important part of an ear recognition system. This paper proposes ear recognition based on Gabor wavelets and Support Vector Machine (SVM). The framework has three steps. In the first step, the ear is detected from an image of the face. In the second step, Gabor wavelets are used to extract ear feature. The Gabor wavelets, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, exhibit desirable characteristics of spatial locality and orientation selectivity. In the third step, when the Gabor wavelets features were obtained, classifications were done by SVM. Research of ear recognition and its application is a new subject in the field of authentication. Ear normalization and alignment is a fundamental module in the ear recognition system.
Keywords
Ear recognition
Keywords
Gabor wavelet
Keywords
support vector machine
Keywords
multi-classification
Journal articles
Eleventh International Conference On Computer Applications (ICCA 2013)
Conference papers
Books/reports/chapters
Thesis/dissertations
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