Finger-vein Image Enhancement and 2D CNN Recognition

Noroz Khan Baloch Noroz (), Saleem Ahmed Saleem and Ramesh Kumar Kumar
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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

International Journal of Innovations in Science & Technology, 2021, vol. 3 special Issue: 4, issue 4, 33-44

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.

Keywords: biometric; contrast limited adaptive histogram equalization; Sobel edge detector; poly region of interest; two dimensional convolution neural network (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:3:y:2021:i:4:p:33-44

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