Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics
Latif, Samihah Abdul
Unknown Affiliation
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Hybrid CNN-ViT integration into Siamese networks for robust iris biometric verification Latif, Samihah Abdul; Sidek, Khairul Azami; Hashim, Aisha Hassan Abdalla
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9182

Abstract

Iris recognition has emerged as a critical biometric verification method, valued for its high accuracy and resistance to forgery. However, traditional convolutional neural network (CNN)-based models, despite their strength in extracting local iris features, struggle to capture global dependencies, which limits their generalization across different datasets. Additionally, conventional classification-based approaches struggle to accurately verify new individuals with limited training data. Thus, this study proposed a hybrid CNN-vision transformer (CNN-ViT) model within a Siamese network to enhance one-shot learning capability by combining CNN’s local feature extraction with vision transformers (ViT’s) global attention. To evaluate its performance, the hybrid model was compared with VGG16 and ResNet under the same training conditions for 20 epochs. VGG16 and ResNet rely on pre-trained models, whereas the hybrid CNN-ViT model is specifically designed to achieve this task with an increment to 98.9% training accuracy, surpassing the TinySiamese model's benchmark accuracy. It also attained a recall of 75%, demonstrating strong sensitivity in correctly identifying positive matches. The hybrid model maintained an excellent balance between learning and generalization by employing the binary cross entropy (BCE) loss function. These findings contribute to the development of efficient iris recognition systems, paving the way for advanced biometric applications in financial transactions, border control and mobile security.
Co-Authors Hashim, Aisha Hassan Abdalla Sidek, Khairul Azami