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A robust 2D-Cochlear transform-based palmprint recognition

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Abstract

In this paper, a noise-robust palmprint recognition system is discussed with a novel feature extraction technique: two-dimensional Cochlear transform (2D-CT) based on the textural analysis of image sample. Orthogonality of 2D-CT is proved which shows the high robustness of the proposed 2D-CT to noise. To validate the proposed feature extraction technique, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, CASIA palmprint database of 312 persons, polyU palmprint database of 386 persons and achieved high accuracy. The proposed 2D-CT method is compared with discriminative and robust competitive code, double orientation code, competitive coding, ordinal coding, Gabor transform, Gaussian membership-based features, absolute average deviation and mean features. Further, K-nearest neighbor is used to validate the matching stage. The results show superiority of the proposed method over other feature extraction methods.

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Acknowledgements

Portions of the research in this paper use the CASIA palmprint database collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA), Hong Kong Polytechnic University (PolyU) palmprint database and Indian Institute of Technology Delhi (IITD) databases. We would like to thank Dr. Harish Parthasarathy, NSUT, for his help and valuable suggestion in completion of this research paper.

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Authors and Affiliations

  1. Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, Delhi, India

    Gopal Chaudhary

  2. Netaji Subhas Institute of Technology, Sector 3, Dwarka, New Delhi, India

    Smriti Srivastava

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  1. Gopal Chaudhary
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  2. Smriti Srivastava
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Correspondence to Gopal Chaudhary.

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Chaudhary, G., Srivastava, S. A robust 2D-Cochlear transform-based palmprint recognition. Soft Comput 24, 2311–2328 (2020). https://doi.org/10.1007/s00500-019-04062-8

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