Proceedings Article10.1109/BCC.2006.4341634
Identification Model for Classifier Combinations
Sergey Tulyakov,Venu Govindaraju +1 more
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TL;DR: A combination method utilizing the dependencies between these scores and showing better performance than comparable traditional combination method using only matching scores related to the claimed identity is presented.
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Abstract: This paper considers combinations of biometric matchers in identification system We assume that the test template is matched not only against the enrolled template of claimed person identity, but also against few enrolled templates of other persons, and all matching scores are available to the combination algorithm We present a combination method utilizing the dependencies between these scores and showing better performance than comparable traditional combination method using only matching scores related to the claimed identity
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Citations
Review of Classifier Combination Methods
Sergey Tulyakov,Stefan Jaeger,Venu Govindaraju,David Doermann +3 more
- 01 Jan 2008
TL;DR: This chapter introduces different categories of classifier combinations and introduces a retraining effect and effects of locality based training as important properties of classifiers combinations.
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Comparison of combination methods utilizing T-normalization and second best score model
Sergey Tulyakov,Zhi Zhang,Venu Govindaraju +2 more
- 23 Jun 2008
TL;DR: The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.
Use of Identification Trial Statistics for the Combination of Biometric Matchers
Sergey Tulyakov,Venu Govindaraju +1 more
TL;DR: The experiments are performed on the National Institute of Standards and Technology BSSR1 dataset and the combination methods considered include the likelihood ratio, neural network, and weighted sum.
Multibiometrics for Human Identification: Predicting Performance in Large-Scale Identification Systems by Score Resampling
Sergey Tulyakov,Venu Govindaraju +1 more
TL;DR: This paper identifies two effects responsible for the prediction errors in previously proposed predictions: the binomial approximation effect and the score mixing effect and proposes a score resampling method for prediction, which is not susceptible to the bino mial approximation effect.
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Learning Matching Score Dependencies for Classifier Combination
Sergey Tulyakov,Venu Govindaraju +1 more
- 01 Jan 2008
TL;DR: This chapter is to investigate the different scenarios of combining classifiers, to show the difficulties in finding the optimal combination algorithms, and to present few possible approaches to combination problems.
2
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