Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Relation between the ROC Curve and the CMC
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Combining Matching Scores in Identification Model
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Models of large population recognition performance
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Mixed group ranks: preference and confidence in classifier combination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
How to handle missing data in robust multi-biometrics verification
International Journal of Biometrics
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Score fusion in multibiometric identification based on fuzzy set theory
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Editor's Choice Article: A survey of approaches and trends in person re-identification
Image and Vision Computing
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Many large-scale biometric systems operate in the identification mode and include multimodal information. While biometric fusion is a well-studied problem, most of the fusion schemes have been implicitly designed for the verification scenario and cannot account for missing data (missing modalities or incomplete score lists) that is commonly encountered in multibiometric identification systems. In this paper, we show that likelihood ratio-based score fusion, which was originally designed for verification systems, can be extended for fusion in the identification scenario under certain assumptions. We further propose a Bayesian approach for consolidating ranks and a hybrid scheme that utilizes both ranks and scores to perform fusion in identification systems. We also demonstrate that the proposed fusion rules can handle missing information without any ad-hoc modifications. We observe that the recognition performance of the simplest rank level fusion scheme, namely, the highest rank method, is comparable to the performance of complex fusion strategies, especially when the goal is not to obtain the best rank-1 accuracy but to just retrieve the top few matches.