Multi-Biometric Fusion of Iris and Face Dynamics using Deep Learning for High-Security Human Identification
The increasing need for reliable human identification systems in secure environments has exposed the limitations of unimodal biometric systems. Individual biometric modalities, including fingerprint, face recognition, and iris scanning, are prone to noise, hardware limitations, spoofing attacks, and data acquisition errors, resulting in high false acceptance and false rejection rates. This paper presents a comprehensive framework for human identification using multi-biometric fusion of iris and face dynamics using a deep learning paradigm. By holistically combining the noise-free properties of the iris with the nonintrusive and globally accessible properties of facial features, the proposed system overcomes the fundamental limitations of unimodal biometric systems while harnessing their complementary benefits. The framework encompasses systematic data acquisition in various environmental settings, sophisticated preprocessing techniques involving normalization and augmentation, robust feature extraction using convolutional neural networks, and multi-level fusion techniques at the feature, score, and decision levels. Experimental results show that the proposed fusion strategy outperforms unimodal biometric systems in recognition accuracy, with a rate of accuracy above 99% and lower error rates. The proposed system is designed for critical applications in banking security, border protection, healthcare, and law enforcement, where identification accuracy and system integrity are paramount.