A robust 2D-Cochlear transform-based palmprint recognition
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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.
KeywordsBiometrics Palmprint Cochlear transform ROI extraction Feature extraction Robustness
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.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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