Multimodal biometric recognition with cancelable template protection using deep learning and an optimized bloom filter

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Md Sabuj Khan, Ting Zhong, Hengjian Li, Fan Zhou

Published: 2026, Last Modified: 15 Jan 2026Appl. Intell. 2026EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Multimodal biometric systems provide advantages over unimodal systems, such as improved accuracy, spoofing resistance, and broader population coverage. However, challenges related to privacy and template protection remain. To address these issues, we propose a integrated framework for multimodal biometric recognition with cancelable template protection, a privacy-preserving mechanism, using deep learning and an optimized bloom filter to significantly enhance recognition performance while ensuring robust security and privacy. First, a key mapping and management system is designed to generate secure keys that support the entire framework. Second, the Dynamic Attention and Hash Network (DAHNet) is employed to extract discriminative palmprint features through a hybrid attention mechanism and a deep hashing network. Third, a quantized fingerprint feature mapping technique is used to generate the corresponding binary fingerprint vector. Finally, the system applies an XOR operation to fuse DAHNet-extracted palmprint features and quantized fingerprint features, followed by an optimized bloom filter to generate secure cancelable templates, ensuring cancelability, irreversibility, and protection against template reconstruction. Experimental evaluations on the TJU and PolyU palmprint datasets, as well as the FVC2002 fingerprint dataset, demonstrate the outstanding accuracy of our state-of-the-art approach, achieving a remarkably low Equal Error Rate (EER). Comparative analysis further shows that the proposed multimodal system significantly outperforms unimodal systems in both recognition accuracy and security. Moreover, security analysis confirms that the framework satisfies all critical requirements for cancelable biometric template protection, including irreversibility, unlinkability, revocability, and robust privacy against various attack scenarios.