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Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems
Abstract
We examine the performance of multimodal biometric authentication systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.
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Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
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Continuous user authentication using multi-modal biometrics
Saevanee, H., Clarke, N., Furnell, S., & Biscione, V. (2015) · Computers & Security · doi:10.1016/j.cose.2015.06.001 · Cited by 109
Cry-based infant pathology classification using GMMs
Farsaie Alaie, H., Abou-Abbas, L., & Tadj, C. (2016) · Speech Communication · doi:10.1016/j.specom.2015.12.001 · Cited by 58
Lee, M.-J., & Choi, W.-Y. (2011) · Journal of the Optical Society of Korea · doi:10.3807/josk.2011.15.1.001 · Cited by 16
Metadata
- Authors
- Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-03
- Volume
- 27
- Issue
- 3
- Pages
- 450-455
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2165956633
- Cited by
- 516
- References
- 1
- FWCI
- 51.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Snelick, R., Uludag, U., Mink, A., Indovina, M., & Jain, A. (2005). Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2005.57
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