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Enhanced Grey Wolf Optimization-Based Support Vector Machine for Multimodal Biometric Border Security System
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Accurate and efficient identity verification is essential for border security. Unimodal biometric systems can be affected by sensor noise, non-universality, inter-class similarities, intra-class variations, and spoof attacks. This paper presents the Enhanced Grey Wolf Optimization-based Support Vector Machine (EGWO-SVM) developed in the study. Support Vector Machine (SVM) penalty parameter C and kernel parameter γ were optimized with the Enhanced Grey Wolf optimizer (EGWO), and the model was trained using face, iris, and fingerprint data from 300 subjects. EGWO represented candidate feature subsets as binary vectors and used a fitness function combining classification error and feature reduction, with α=0.9 and β=0.1. An RBF-kernel SVM was then optimized using k-fold cross-validation. At the 0.51 threshold, the multimodal EGWO-SVM achieved FAR of 0.67%, FRR of 2.67%, F1-score of 98.32%, recognition accuracy of 98.33%, and computational time of 121.31 s, compared with 4.00%, 6.00%, 5.45%, 94.95%, 95.00%, and 147.68 s for SVM. The findings indicate that the Tent-map enhancement improved the optimization-based biometric classification framework.
Keywords
multimodal biometrics; border security; grey wolf optimization (GWO); tent chaotic map; support vector machine
References
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How to cite this paper
@article{1723797,
author = {OJO Omotayo Job, ADETUNJI Abigail Bola, ALADE Oluwaseun Modupe, ADEDAYO-AJAYI Victoria Oluwaseyi, JEREMIAH Yetomiwa Sinat},
title = {Enhanced Grey Wolf Optimization-Based Support Vector Machine for Multimodal Biometric Border Security System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {1361-1370},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723797.pdf},
abstract = {Accurate and efficient identity verification is essential for border security. Unimodal biometric systems can be affected by sensor noise, non-universality, inter-class similarities, intra-class variations, and spoof attacks. This paper presents the Enhanced Grey Wolf Optimization-based Support Vector Machine (EGWO-SVM) developed in the study. Support Vector Machine (SVM) penalty parameter C and kernel parameter γ were optimized with the Enhanced Grey Wolf optimizer (EGWO), and the model was trained using face, iris, and fingerprint data from 300 subjects. EGWO represented candidate feature subsets as binary vectors and used a fitness function combining classification error and feature reduction, with α=0.9 and β=0.1. An RBF-kernel SVM was then optimized using k-fold cross-validation. At the 0.51 threshold, the multimodal EGWO-SVM achieved FAR of 0.67%, FRR of 2.67%, F1-score of 98.32%, recognition accuracy of 98.33%, and computational time of 121.31 s, compared with 4.00%, 6.00%, 5.45%, 94.95%, 95.00%, and 147.68 s for SVM. The findings indicate that the Tent-map enhancement improved the optimization-based biometric classification framework.},
keywords = {multimodal biometrics; border security; grey wolf optimization (GWO); tent chaotic map; support vector machine},
month = {October},
}