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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 3-4/2017

21.01.2017 | Original Paper

Collaborative representation with HM-LBP features for palmprint recognition

verfasst von: Xiumei Guo, Weidong Zhou, Yanli Zhang

Erschienen in: Machine Vision and Applications | Ausgabe 3-4/2017

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Abstract

A novel collaborative representation model with hierarchical multiscale local binary pattern (HM-LBP) for palmprint recognition is proposed in this paper. HM-LBP can retrieve useful information from non-uniform patterns and reduce the influence of gray scale, rotation and illumination. The HM-LBP feature of palmprint is extracted, and its dimension is reduced by principal component analysis. And then, a collaborative classification with HM-LBP is presented to fully exploit the discrimination information. The proposed algorithm is evaluated on the Hong Kong Polytechnic University database (v2) to test its feasibility and performance. The results show that the algorithm can achieve ideal recognition accuracy of 100% and the speediness is able to fit for the real-time palmprint recognition system.
Vorheriger Artikel Image-guided ToF depth upsampling: a survey
Nächster Artikel Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation

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Metadaten
Titel
Collaborative representation with HM-LBP features for palmprint recognition
verfasst von
Xiumei Guo
Weidong Zhou
Yanli Zhang
Publikationsdatum
21.01.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 3-4/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0821-y

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