Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

Selma Elnasir; Siti Mariyam Shamsuddin; Sajad Farokhi

doi:10.1117/1.JEI.24.1.013031

19 February 2015 Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

Selma Elnasir, Siti Mariyam Shamsuddin, Sajad Farokhi

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013031 (February 2015). https://doi.org/10.1117/1.JEI.24.1.013031

Abstract

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases—PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint—show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

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Selma Elnasir, Siti Mariyam Shamsuddin, and Sajad Farokhi "Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis," Journal of Electronic Imaging 24(1), 013031 (19 February 2015). https://doi.org/10.1117/1.JEI.24.1.013031

Published: 19 February 2015

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