Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

Authors

  • Noroz Khan Baloch Noroz Dawood University of Engineering and Technology
  • Saleem Dept. of Computer System Engg. Dawood University of Engineering and technology Karachi, Pakistan.(
  • Ramesh Dept. of Computer System Engg. Dawood University of Engineering and technology Karachi, Pakistan
  • Saqib Dept. of Telecommunication at Dawood University of Engineering and Technology Karachi, Pakistan.
  • Yawar Dept. of Electronic, NED UET Karachi, Pakistan.

DOI:

https://doi.org/10.30537/sjcms.v6i1.1001

Keywords:

Two dimensional convolutional neural network, dual contrast limited adaptive histogram equalization.

Abstract

The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

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Author Biographies

Saleem, Dept. of Computer System Engg. Dawood University of Engineering and technology Karachi, Pakistan.(

Associate Professor / Chairman.

Ramesh, Dept. of Computer System Engg. Dawood University of Engineering and technology Karachi, Pakistan

Assistant Professor.

Saqib, Dept. of Telecommunication at Dawood University of Engineering and Technology Karachi, Pakistan.

Assistant Professor/Chairman.

Yawar, Dept. of Electronic, NED UET Karachi, Pakistan.

Assistant Professor. Electronic Department, NEDUET.

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Published

2022-07-21