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

Autor: J. Raja Sekar, G. Ananthi, S. Arivazhagan
Rok vydání: 2021
Předmět:
Zdroj: The Visual Computer. 38:1901-1914
ISSN: 1432-2315
0178-2789
DOI: 10.1007/s00371-021-02253-9
Popis: 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.
Databáze: OpenAIRE
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