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.INDEX TERMS Feature-level fusion, multimodal biometrics, privacy and security. I. INTRODUCTION 19 Biometric recognition systems utilize the physical or behav-20 ioral characteristics of an individual for identity manage-21 ment. These characteristics include fingerprint, face, iris, 22 hand/finger geometry, retina, gait, palmprint, voice pattern, 23 signature, ear, hand/finger-vein, odor, or even the DNA 24 information of an individual [1]. Recently, it has become 25 157 accuracy of an unprotected score-level fusion. 158 In [20], Chin et al. proposed a feature-level fusion BTP 159 system based on fingerprint and palmprint. In the scheme, the 160 normalized fingerprint and palmprint templates are first fused 161 into a single feature using concatenation. The fused feature is 162 then transformed into another form (i.e., RT feature) using a 163 parameterized function known as Random Tiling (RT). The 164 RT function extracts a set of random rectangles from the 165 fused template using a user-specific key, and a feature vector 166 is formed by concatenating the standard deviation of each 167 rectangle. This key allows the scheme to be revocable by 168 replacing the key once there is a leak or breach of security. 169 Finally, a bit-string template is formed as the final cancellable 170 template by binarizing the RT feature using an equa...