Research Article
Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification
@ARTICLE{10.4108/eetcasa.v9i1.3708, author={Nguyen Kim Quoc and Ha Minh Tan and Dang Nhu Phu and Vuong Xuan Chi and Phan Cong Vinh}, title={Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={9}, number={1}, publisher={EAI}, journal_a={CASA}, year={2023}, month={8}, keywords={iris recognition, periocular recognition, iris segmentation, iris biometric, personal identification}, doi={10.4108/eetcasa.v9i1.3708} }- Nguyen Kim Quoc
Ha Minh Tan
Dang Nhu Phu
Vuong Xuan Chi
Phan Cong Vinh
Year: 2023
Advancements in Iris Recognition: WAHET-CNN Framework for Accurate Segmentation and Pattern Classification
CASA
EAI
DOI: 10.4108/eetcasa.v9i1.3708
Abstract
Biometric and identification patterns have gained extensive research and application, particularly in iris recognition. The iris harbors a wealth of individual-specific information, making it a vital element in biometric authentication. This article presents a comprehensive study encompassing iris segmentation and identification. We introduce the Weighted Adaptive Hough Ellipsopolar Transform Convolutional Neural Network (WAHET-CNN) as a novel approach for classifying pattern images. Our experimental outcomes demonstrate a commendable 90% accuracy achieved by the proposed WAHET-CNN on the CASIA dataset Version 4.
Copyright © 2023 N. K. Quoc et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
