Reinforced Deep Learning for Verifying Finger Veins

Authors

  • Shaima Miqdad Mohamed Najeeb Northern Technical University
  • Raid Rafi Omar Al-Nima Northern Technical University
  • Mohand Lokman Ahmad Al-Dabag Northern technical university

DOI:

https://doi.org/10.3991/ijoe.v17i07.24655

Keywords:

Finger Veins, Verification, Deep Learning

Abstract


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.

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Published

2021-07-02

How to Cite

Najeeb, S. M. M., Al-Nima, R. R. O., & Al-Dabag, M. L. A. (2021). Reinforced Deep Learning for Verifying Finger Veins. International Journal of Online and Biomedical Engineering (iJOE), 17(07), pp. 19–27. https://doi.org/10.3991/ijoe.v17i07.24655

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Section

Papers