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Palmprint identification using sparse and dense hybrid representation

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Abstract

Among various palmprint identification methods proposed in the literature, Sparse Representation for Classification (SRC) is very attractive, offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In fact, palmprint images do not only contain identity information but they also have other information such as illumination and distortions due the acquisition conditions. In this case, SRC may not be able to classify the identity of palmprint well in the original space since samples from the same class show large variations. To overcome this problem, we propose in this work to exploit sparse-and-dense hybrid representation (SDR) for palmprint identification. Indeed, this type of representations that are based on the dictionary learning from the training data has shown its great advantage to overcome the limitations of SRC. Extensive experiments are conducted on two publicly available palmprint datasets: multispectral and PolyU. The obtained results clearly show the ability of the proposed method to outperform both the state-of-the-art holistic approaches and the coding palmprint identification methods.

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Notes

  1. www4.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm

  2. www4.comp.polyu.edu.hk/~biometrics/index.htm

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Acknowledgments

This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Somaya Al Maadeed & Imad Rida

  2. School of Electrical and Electronics Engineering, Nanyang Technological University, Nanyang Link, Singapore, 639798, Singapore

    Xudong Jiang

  3. Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, UK

    Ahmed Bouridane

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  1. Somaya Al Maadeed
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  4. Ahmed Bouridane
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Correspondence to Somaya Al Maadeed.

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Maadeed, S.A., Jiang, X., Rida, I. et al. Palmprint identification using sparse and dense hybrid representation. Multimed Tools Appl 78, 5665–5679 (2019). https://doi.org/10.1007/s11042-018-5655-8

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  • DOI: https://doi.org/10.1007/s11042-018-5655-8

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