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Human palm vein authentication using curvelet multiresolution features and score level fusion

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Abstract

Human authentication plays a crucial role in sensitive applications like ATM usage, entry into a secured area, attendance and many more. A novel human authentication system is proposed by extracting curvelet multiresolution features from the palm vein trait. The entire palm region is extracted by using an improved bounding rectangle strategy and is further enhanced using Difference of Gaussian (DoG) and Histogram Equalization (HE) methods in order to make the vein pattern, more prominent. Curvelet, a multiresolution transform which handles curve discontinuities well is applied with five scales and sixteen orientations over the enhanced palm vein region. Standard deviation and mean features are calculated from the obtained curvelet subbands. Two scores are computed from these individual features and finally fused using weighted sum rule. The experiments are conducted in publicly available CASIA and VERA palm vein databases which results with the recognition rate of 99.7% and 99.86%, respectively. The proposed system achieved the lowest equal error rate (EER) of 0.021% and 0.0207%, respectively, for CASIA and VERA palm vein database as compared with other state-of-the-art methods. The system performance measured in terms of computation time took a maximum of 0.09 s in identifying an individual.

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Acknowledgements

The authors wish to thank Chinese Academy of Sciences’ Institute of Automation for providing the CASIA-MS-PalmprintV1.0. One half of the work was carried out with this dataset. The authors also thank Idiap Research Institute, Martigny, Switzerland and Pedro Tome, Sebastien Marcel, the inventors of VERA Palm vein database. With this VERA Palm vein database, the authors completed another half of the work.

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  1. Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, 626 005, India

    G. Ananthi, J. Raja Sekar & S. Arivazhagan

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  1. G. Ananthi
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Ananthi, G., Sekar, J.R. & Arivazhagan, S. Human palm vein authentication using curvelet multiresolution features and score level fusion. Vis Comput 38, 1901–1914 (2022). https://doi.org/10.1007/s00371-021-02253-9

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