IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Comparison of Face Verification Results on the XM2VTS Database
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Error analysis of pattern recognition systems: the subsets bootstrap
Computer Vision and Image Understanding
Score normalization in multimodal biometric systems
Pattern Recognition
Exploiting global and local decisions for multimodal biometrics verification
IEEE Transactions on Signal Processing - Part II
Image and Vision Computing
Confidence partition and hybrid fusion in multimodal biometric verification system
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
SDUMLA-HMT: a multimodal biometric database
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Study of applicability of virtual users in evaluating multimodal biometrics
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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Combining multiple information sources, typically from several data streams is a very promising approach, both in experiments and to some extent in various real-life applications. A system that uses more than one behavioral and physiological characteristics to verify whether a person is who he/she claims to be is called a multimodal biometric authentication system. Due to lack of large true multimodal biometric datasets, the biometric trait of a user from a database is often combined with another different biometric trait of yet another user, thus creating a so-called chimeric user. In the literature, this practice is justified based on the fact that the underlying biometric traits to be combined are assumed to be independent of each other given the user. To the best of our knowledge, there is no literature that approves or disapproves such practice. We study this topic from two aspects: 1) by clarifying the mentioned independence assumption and 2) by constructing a pool of chimeric users from a pool of true modality matched users (or simply “true users”) taken from a bimodal database, such that the performance variability due to chimeric user can be compared with that due to true users. The experimental results suggest that for a large proportion of the experiments, such practice is indeed questionable.