Advanced Biometric Iris Authentication System Leveraging Deep Learning Techniques
R Breesha, Menaka M, C D Merlin, D. Janet Ramya, R. Sakthisri, M. Thirisha · 2025
This paper provides an advanced biometric iris authentication system using deep learning, particularly Convolutional Neural Networks (CNNs), in order to enhance the performance as well as efficiency of iris-based biometrics. The proposed system overcomes a few of the shortcomings of traditional methods of iris authentication, such as susceptibility to spoofing and reduced accuracy when lighting conditions change. A very large dataset of iris images was preprocessed to minimize variations, ensuring the best possible accuracy in recognition of iris patterns. The system outperformed the state-of-the-art systems in terms of False Acceptance Rate (FAR) and False Rejection Rate (FRR). Furthermore, high-level spoofing detection capabilities were added, significantly lowering vulnerability to attacks using fake iris images. One of the significant contributions of this work is creating a particular CNN architecture for iris recognition and a novel preprocessing pipeline for more improved feature extraction. Results show that the proposed deep learning-based approach outperforms the already existing systems, so it is a promising solution for secure and privacy-preserving human identification in real-world applications. This research will serve as a seed to future studies in fine-tuning deep learning models applied to different security domains in biometric systems.