Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
Unobtrusive user identification with light biometrics
Proceedings of the third Nordic conference on Human-computer interaction
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapted user-dependent multimodal biometric authentication exploiting general information
Pattern Recognition Letters
Combining face and iris biometrics for identity verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Can chimeric persons be used in multimodal biometric authentication experiments?
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Towards predicting optimal fusion candidates: a case study on biometric authentication tasks
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
Multimodal speaker identification using an adaptive classifier cascade based on modality reliability
IEEE Transactions on Multimedia
Multimodal decision-level fusion for person authentication
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Unobtrusive user authentication is more convenient than explicit interaction and can also increase system security because it can be performed frequently, unlike the current ''once explicitly and for a long time'' practice. Existing unobtrusive biometrics (e.g., face, voice, gait) do not perform sufficiently well for high-security applications, however, while reliable biometric authentication (e.g., fingerprint or iris) requires explicit user interaction. This work presents experiments with a cascaded multimodal biometric system, which first performs unobtrusive user authentication and requires explicit interaction only when the unobtrusive authentication fails. Experimental results obtained for a database of 150 users show that even with a fairly low performance of unobtrusive modalities (Equal Error Rate above 10%), the cascaded system is capable of satisfying a security requirement of a False Acceptance Rate less than 0.1% with an overall False Rejection Rate of less than 0.2%, while authenticating unobtrusively in 65% of cases.