AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption
This work addresses privacy protection in biometric authentication for high-security applications, though it appears incremental as it builds on existing encryption and fusion techniques.
The paper tackles the problem of balancing security and usability in multi-biometric systems by proposing AMB-FHE, an adaptive fusion method with fully homomorphic encryption, which was benchmarked on a bimodal database of iris and fingerprint datasets using deep neural networks.
Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.