Development of A Palm-Vein Recognition System for Identification and Verification Systems using Enhanced Convolutional Neural Network

  • Dare S. Olayiwola DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, LADOKE AKINTOLA UNIVERSITY OF TECHNOLOGY. OGBOMOSO
  • Abisola A. Olayiwola
  • Jonathan P. Oguntoye
  • Olayanju O. AWODOYE
  • Adesina R. Ganiyu
  • Olusayo E. Omidiora
Keywords: Gravitational Search Algorithm, Recognition System, Biometric Authentication, Convolutional Neural Network, Hyperparameter Optimization, Palm-Vein Recognition

Abstract

Biometric authentication systems have gained significant attention in access control applications due to their ability to provide enhanced security and convenience. Among various biometric modalities, palm-vein recognition has emerged as a promising approach, offering high accuracy, reliability, and resistance to forgery. However, existing palm-vein recognition systems often face challenges in implementation costs, computational efficiency, and performance limitations. This research aimed to develop an enhanced palm-vein recognition system for access control applications by optimizing a Convolutional Neural Network (CNN) architecture. A palm-vein dataset comprising 1000 images from 200 LAUTECH students was acquired, with 5 images per individual. The dataset was split into 700 training images and 300 testing images. The acquired images were pre-processed for quality enhancement and region of interest extraction. A Gravitational Search Algorithm (GSA) optimized CNN (GSA-CNN) was then employed to extract deep features from the pre-processed images, which were classified using a SoftMax layer. Experimental results revealed that the CNN technique achieved a specificity, sensitivity, False Positive Rate (FPR), accuracy of 74.60%, 79.89%, 25.40%, 77.67% at 117.52 seconds, respectively. In contrast, the proposed GSA-CNN technique demonstrated superior performance, achieving a specificity, sensitivity, FPR, accuracy of 92.06%, 92.53%, 7.94%, 92.33% at 97.14 seconds, respectively. The GSA-CNN system outperformed the conventional CNN approach in terms of accuracy, specificity, sensitivity, FPR, and processing time, demonstrating its potential for reliable and efficient palm-vein recognition in access control applications. The findings have significant implications for developing robust and secure access control systems, contributing to enhanced privacy and security across various domains.
Published
2024-03-30