NDLI: A kind of correlation classification distance of whole phase based on weight

Content Provider	IEEE Xplore Digital Library
Author	Liu Yang He Yan Yue Xue Dong Liu Ying Fei
Copyright Year	2010
Description	Author affiliation: School of Physics and Engineering, Zhengzhou University, China (Yue Xue Dong) \|\| School of Computer and Communications Engineering, Zhengzhou University of Light Industry, China (Liu Yang; He Yan; Liu Ying Fei)
Abstract	In the process of iris classification, a new classification distance with adjustable weight which takes advantage of whole phase information to encode is proposed. The method is to use feature extraction function to do the extraction toward all iris image, which could obtain real and imaginary part iris information. Then, tangent function is used to transform extracted real and imaginary part characteristics into phase information and adjust the phase angle to the range (−π + π). The eigenvector that contained all image phase angle information will be set up to do with the weighted inner product and normalization processing to obtain normalized classification distance between the iris samples. The experiment shows this algorithm could modify the 4-quadrant phase encoding problem that might omit details of the phase information, and the classification effect is encouraged.
Starting Page	668
Ending Page	671
File Size	1041387
Page Count	4
File Format	PDF
ISBN	9781424473878
e-ISBN	9781424473908
DOI	10.1109/ESIAT.2010.5568438
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Correlation Image recognition iris classification distance feature extraction Feature extraction whole phase Classification algorithms Character recognition Iris recognition
Content Type	Text
Resource Type	Article

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