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A Palmprint Classification Method based on Local Sparse Coding

Authors: Dongliang Yang; Changjiang Song; Gang Wu;

A Palmprint Classification Method based on Local Sparse Coding

Abstract

A palmprint classification method based on local sparse coding is presented. Firstly, patch-wise statistics based local binary patterns features are as input of local sparse coding classifier. And then the method finds k samples, which are similar with the test sample. Finally the sparse representation classifier is applied to the test sample and the k samples to determine the classification information of the test sample in the database. The experimental results on PolyU palmprint database show that new method has higher classification accuracy than the tradition methods.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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