The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Likelihood Ratio-Based Biometric Score Fusion
Abstract
Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle (i) discrete values in biometric match score distributions, (ii) arbitrary scales and distributions of match scores, (iii) correlation between the scores of multiple matchers and (iv) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
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Cites
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Density Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,525
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,290
Testing Statistical Hypotheses
(2021) · Wiley Series in Probability and Statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,106
Score normalization in multimodal biometric systems
Jain, A., Nandakumar, K., & Ross, A. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.01.012 · Cited by 2,154
Self-citationUnsupervised learning of finite mixture models
Figueiredo, M. A. T., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990138 · Cited by 2,125
Self-citation(2006) · International Series on Biometrics · doi:10.1007/0-387-33123-9 · Cited by 1,189
Testing Statistical Hypotheses
Neath, A. A. (2006) · Journal of the American Statistical Association · doi:10.1198/jasa.2006.s100 · Cited by 948
Person identification using multiple cues
Brunelli, R., & Falavigna, D. (1995) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.464560 · Cited by 592
Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems
Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.57 · Cited by 516
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Security Evaluation of Pattern Classifiers under Attack
Biggio, B., Fumera, G., & Roli, F. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.57 · Cited by 411
Query-adaptive late fusion for image search and person re-identification
Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., & Tian, Q. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298783 · Cited by 292
Who Are You? A Statistical Approach to Measuring User Authenticity
Freeman, D., Jain, S., Duermuth, M., Biggio, B., & Giacinto, G. (2016) · Proceedings 2016 Network and Distributed System Security Symposium · doi:10.14722/ndss.2016.23240 · Cited by 123
Automated face recognition in forensic science: Review and perspectives
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Metadata
- Authors
- Nandakumar, K., Chen, Y., Dass, S. C., & Jain, A. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-02
- Volume
- 30
- Issue
- 2
- Pages
- 342-347
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2136885397
- Cited by
- 516
- References
- 14
- FWCI
- 43.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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