The widespread use of biometric identification systems has raised important issues regarding template security and scalability. In contrast to passwords, biometric characteristics, once exposed, cannot be withdrawn, thus raising permanent risks of identity exposure. Moreover, traditional systems require complete retraining when adding new users, thus causing computational inefficiency and low scalability. This work presents an original framework for cancelable multi-biometric identification that combines face and iris dynamics in an incremental deep learning model. The proposed method produces non-invertible and revocable templates through deep feature extraction by ResNet-50, followed by a random projection transformation, thus meeting the ISO/IEC 24745:2022 security requirements. A dynamic 1D-CNN model, enhanced by elastic weight consolidation and rehearsal learning, enables incremental learning without catastrophic forgetting or complete model retraining. Experimental evaluation shows an average recognition rate of 98.98% on incremental datasets, with false acceptance rates as low as 0.115% and true acceptance rates of 98.93%. The proposed framework provides an optimal trade-off among security, scalability, and recognition performance, thus filling the most important gaps in current biometric systems and offering a basis for the development of a next-generation privacy-preserving identification infrastructure.
Shaik Badulla, Vundavalli Balasankar, G. V. Vara Prasad· 2026 4th International Confe...· 0 citations
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.
Shaik Badulla, Vundavalli Balasankar, G. L. V. Prasad· 2026 7th International Confe...· 0 citations