Email this article CNN-based hybrid fusion for robust multimodal Face-Iris biometric authentication system
Abstract
This research presents a robust CNN driven biometric authentication system that combines face and iris recognition through both feature-level and score-level fusion. The framework addresses key limitations of unimodal systems which includes pose variation, lighting inconsistencies and spoofing by leveraging the strengths of each biometric trait. Two parallel CNN branches extract deep features from face and iris images, which are then fused and classified. Simultaneously, similarity scores from individual classifiers are combined using a weighted average. A hybrid decision rule integrates both outputs to enhance reliability and reduce false acceptances. The model was tested on the ORL and CASIA-IrisV4 datasets under realistic conditions. It achieved a recognition accuracy of 99.65% and a 0.00% FAR, outperforming unimodal and single-fusion approaches. This confirms the system’s potential for high-security applications. Future research will explore scalability with larger datasets, inclusion of additional modalities like fingerprint and deployment on mobile or edge devices.This research presents a robust CNN driven biometric authentication system that combines face and iris recognition through both feature-level and score-level fusion. The framework addresses key limitations of unimodal systems which includes pose variation, lighting inconsistencies and spoofing by leveraging the strengths of each biometric trait. Two parallel CNN branches extract deep features from face and iris images, which are then fused and classified. Simultaneously, similarity scores from individual classifiers are combined using a weighted average. A hybrid decision rule integrates both outputs to enhance reliability and reduce false acceptances. The model was tested on the ORL and CASIA-IrisV4 datasets under realistic conditions. It achieved a recognition accuracy of 99.65% and a 0.00% FAR, outperforming unimodal and single-fusion approaches. This confirms the system’s potential for high-security applications. Future research will explore scalability with larger datasets, inclusion of additional modalities like fingerprint and deployment on mobile or edge devices.
Received on, 01 June 2025
Accepted on, 06 July 2025
Published on, 06 October 2025
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DOI: https://dx.doi.org/10.21622/ACE.2025.05.2.1406
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Copyright (c) 2025 Afolabi Ifedayo Awodeyi, Philip Asuquo, Bliss Utibe-Abasi Stephen
Advances in Computing and Engineering
E-ISSN: 2735-5985
P-ISSN: 2735-5977
Published by:
Academy Publishing Center (APC)
Arab Academy for Science, Technology and Maritime Transport (AASTMT)
Alexandria, Egypt
