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A multimodal biometric authentication framework based on Index-of-Max (IoM) hashing, Alignment-Free Hashing (AFH), and feature-level fusion is proposed in this paper. This framework enjoys three major merits: 1) Biometric templates are secured by biometric template protection technology (i.e., IoM hashing), thus providing strong resistance to security and privacy invasion; 2) It flexibly adopts a variety of biometric feature representations (e.g., binary, and real-valued), thus generalizing to a wide range of biometric features for fusion; 3) Feature-level fusion, which has low template storage, low matching computational complexity, and low privacy risks, which can be accomplished without alignment via AFH. Specifically, the proposed framework works as a drag-and-drop model that can quickly adopt all popular biometric modalities with different feature distributions for feature-level fusion. The fused templates are produced using operators AND, OR and XOR in binary domain. To evaluate the proposed framework, benchmarking datasets from four widely deployed biometric modalities (i.e., FVC 2002 for fingerprint, LFW for face, CASIA-v3-Interval for iris, and UTFVP for finger-vein) are used. The experimental results presented in Table 5 suggest that the proposed framework can achieve state-of-the-art performance in most of the datasets while offering additional folds, such as template protection and generalization to variable features. Moreover, biometric template protection criteria (irreversibility, unlinkability, and revocability) are also analyzed. The results of the analysis indicate satisfaction in terms of the security and privacy of the templates generated from the proposed framework. |
