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Finger-vein Image Enhancement and 2D CNN Recognition

Author

Listed:
  • Noroz Khan Baloch Noroz

    (Dept. of Electronics Engg. Dawood University of Engineering & Technology Karachi, Pakistan.)

  • Saleem Ahmed Saleem

    (Dept. of Computer System Engg. Dawood University of Engineering & Technology Karachi, Pakistan)

  • Ramesh Kumar Kumar

    (Dept. of Computer System Engg. Dawood University of Engineering & Technology Karachi, Pakistan)

Abstract

Finger vein recognition technology is a novel biometric technology with multiple features such as live capture, stability, difficulty in stealing and imitating, and more in the field of information security that has been utilized in a wide range of applications. In this proposed method, the finger region is separated from the background using a Sobel Edge detector and a Poly ROI which helps shape the finger. The background separation enhancement of low contrast using dual contrast limited adaptive histogram equalization which works on the visual characteristics of the finger-vein image dataset. When dual CLAHE is applied, the finger-vein histogram intensity is separated all across the image. Following the implementation of DCLAHE, an enhanced 2D-CNN model is utilized to recognize objects with the updated dataset. By maximizing the values of a preprocessed dataset, the 2D CNN model learns features. This model has a 94.88% accuracy rate.

Suggested Citation

  • Noroz Khan Baloch Noroz & Saleem Ahmed Saleem & Ramesh Kumar Kumar, 2021. "Finger-vein Image Enhancement and 2D CNN Recognition," International Journal of Innovations in Science & Technology, 50sea, vol. 3(4), pages 33-44, December.
    • Handle: RePEc:abq:ijist1:v:3:y:2021:i:4:p:33-44
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