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Multi-order texture features for palmprint recognition

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Abstract

Palmprint attracts increasing attention thanks to its several advantages. 1st-order textures have been widely used for palmprint recognition; unfortunately, high-order textures, although they are also discriminative, were ignored in the existing works. 2nd-order textures are first employed for palmprint recognition in this paper. 1st-order textures are convolved with the filters to extract 2nd-order textures that can refine the texture information and improve the contrast of the feature map. Then 2nd-order textures are used to generate 2nd-order Texture Co-occurrence Code (2TCC). The sufficient experiments demonstrate that 2TCC yields satisfactory accuracy performance on four public databases, including contact, contactless and multi-spectral acquisition types. Moreover, in order to further improve the discrimination and robustness of 2TCC, we propose Multiple-order Texture Co-occurrence Code (MTCC), in which 1st-order Texture Co-occurrence Code (1TCC) and 2TCC are fused at score level. 1TCC is good at describing minor wrinkles; while 2TCC does well in describing principal textures. Thus the combination of both can describe the palmprint features more comprehensively. MTCC achieves remarkable accuracy performance when compared with the state-of-the-art methods on all public databases.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Notes

  1. Available at https://www.comp.polyu.edu.hk/biometrics, last accessed on June 20, 2021

  2. Available at https://www.comp.polyu.edu.hk/biometrics, last accessed on June 20, 2021

  3. Available at https://www.comp.polyu.edu.hk/csajaykr/IITD/Database/Palm.htm, last accessed on June 20, 2021

  4. Available at https://www.cslinzhang.github.io/ContactlessPalm/, last accessed on June 20, 2021

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Funding

This work was supported in part by the National Natural Science Foundation of China by the National Natural Science Foundation of China under Grants 61866028, 61866025 and 62162045, Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science and Technology Department of Jiangxi Province) under Grant 20212BDH81003 and the Key Program Project of Research and Development by the Jiangxi Provincial Department of Science and Technology under Grant 20192BBE50073.

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Authors and Affiliations

  1. School of Software, Nanchang Hangkong University, No. 696 Fenghe Nan Avenue, Nanchang, 330063, Jiangxi, China

    Lu Leng, Tengfei Wu & Jun Chu

  2. College of Computer Sciences, Sichuan University, No.24 South Section, First Ring Road, Chengdu, 610065, Sichuan, China

    Ziyuan Yang

  3. School of Information Engineering, Nanchang Hangkong University, No. 696 Fenghe Nan Avenue, Nanchang, 330063, Jiangxi, China

    Ming Li

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  1. Ziyuan Yang
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  2. Lu Leng
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  3. Tengfei Wu
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  4. Ming Li
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  5. Jun Chu
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Correspondence to Lu Leng.

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Related code are released immediately when this paper is accepted at https://github.com/Zi-YuanYang/MTCC-2TCC.

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Yang, Z., Leng, L., Wu, T. et al. Multi-order texture features for palmprint recognition. Artif Intell Rev 56, 995–1011 (2023). https://doi.org/10.1007/s10462-022-10194-5

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