Reinforced Deep Learning for Verifying Finger Veins

Shaima Miqdad Mohamed Najeeb, Raid Rafi Omar Al-Nima, Mohand Lokman Ahmad Al-Dabag


Recently, personal verifications become crucial demands for providing securities in personal accounts and financial activities. This paper suggests a new Deep Learning (DL) model called the Re-enforced Deep Learning (RDL). This approach provides another way of personal verification by using the Finger Veins (FVs). The RDL consists of multiple layers with a feedback. Two FV fingers are employed for each person, FV of the index finger for first personal verification and FV of the middle finger for re-enforced verification. The used database is from the Hong Kong Polytechnic University Finger Image (PolyUFI) database (Version 1.0). The result shows that the proposed RDL achieved a promising performance of 91.19%. Also, other DL approaches are exploited for comparisons in this study including state-of-the-art models.


Finger Veins; Verification; Deep Learning

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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