Mixing Binary Face and Fingerprint based on Extended Feature Vector (EFV) Hashing Multimodal biometric template protection (BTP) is gaining increasing attention as it overcomes the drawback of unimodal BTP. In this work, a tokenless cancellable biometric scheme, namely Extended Feature Vector (EFV) Hashing, is applied to fuse the multimodal binary face and fingerprint template at a featurelevel fusion. In advance to the unimodal EFV hashing, this work reorganizes the feature extension mechanism in EFV transformation and introduces the random sampling mechanism to increase the difficulty for reverse processing of the cancellable template. In addition, two fusion options are developed for producing the cancellable template. Experiments are conducted on the wellknown Fingerprint FVC2004 and Face LFW datasets. The results demonstrate that the propose approach achieves a satisfactory verification rate with EER  0.1%.

Multibiometrics User Recognition using Adaptive Cohort Ranking Personal identification using multibiometrics is desirable in a wide range of highsecurity andor forensic application as it can address performance limitations from unimodal biometrics systems. This paper presents a new scheme the multibiometrics fusion to achieve performance improvement for the user identificationrecognition. We model the biometric identification solution using an adaptive cohort ranking approach, which can more effectively utilize the cohort information for maximizing the true positive identification rates. In contrast to the tradition cohortbased methods, the proposed cohort ranking approach offers merit of being matcher independence as it does not make any assumption on the nature of score distributions from any of the biometric matcher(s). In addition, our scheme is adaptive and can be incorporated for any biometric matchertechnologies. The proposed approach is evaluated on publicly available unimodal and multimodal biometrics databases, i.e., BSSR1 multimodal matching scores for fingerprint and face matchers and XM2VTS matching scores from synchronize databases of face and voice. In both the unimodal and multimodal databases, our results indicate that the proposed approach can outperform the conventional adaptive identification approaches. The experimental results from both public databases are quite promising and validate the contributions from this work.