NDLI: Weighted Linear Embedding and Its Applications to Finger-Knuckle-Print and Palmprint Recognition

Content Provider	IEEE Xplore Digital Library
Author	Jun Yin Jingbo Zhou Zhong Jin Jian Yang
Copyright Year	2010
Abstract	In this paper we propose a new linear feature extraction approach called Weighted Linear Embedding (WLE). WLE combines Fisher criterion with manifold learning criterion like local discriminant embedding analysis (LDE), whereas unlike LDE that only utilizes local neighbor information it uses local information and nonlocal information simultaneously. WLE is also unlike linear discriminant analysis (LDA) that treats local information and nonlocal information equally, and it uses these two kinds of information discriminatively by utilizing the Gaussian weighting. Hence, WLE is more powerful than LDA and LDE for feature extraction. Experimental results on the PolyU finger-knuckle-print database and the PolyU palmprint database indicate that our WLE algorithm outperforms principal components analysis (PCA), LDA and LDE.
Starting Page	1
Ending Page	4
File Size	397642
Page Count	4
File Format	PDF
ISBN	9781424470631
e-ISBN	9781424470655
DOI	10.1109/ETCHB.2010.5559291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-22
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Manifolds Fingers Feature extraction Indexes Principal component analysis
Content Type	Text
Resource Type	Article

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Weighted Linear Embedding and Its Applications to Finger-Knuckle-Print and Palmprint Recognition