The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
MCYT baseline corpus: a bimodal biometric database
Abstract
The current need for large multimodal databases to evaluate automatic biometric recognition systems has motivated the development of the MCYT bimodal database. The main purpose has been to consider a large scale population, with statistical significance, in a real multimodal procedure, and including several sources of variability that can be found in real environments. The acquisition process, contents and availability of the single-session baseline corpus are fully described. Some experiments showing consistency of data through the different acquisition sites and assessing data quality are also presented.
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Cites
The DET curve in assessment of detection task performance
Martin, A., Doddington, G., Kamm, T., Ordowski, M., & Przybocki, M. (1997) · 5th European Conference on Speech Communication and Technology (Eurospeech 1997) · doi:10.21437/eurospeech.1997-504 · Cited by 1,384
XM2VTSDB: The Extended M2VTS Database
Messer, K., Matas, J., Kittler, J., Luettin, J., & Maître, G. (1999) · Cited by 1,158
FVC2000: fingerprint verification competition
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990140 · Cited by 805
Some statistical issues in the comparison of speech recognition algorithms
Gillick, L., & Cox, S. J. (2003) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1989.266481 · Cited by 636
On-line signature verification
Jain, A. K., Griess, F. D., & Connell, S. D. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00240-0 · Cited by 536
What size test set gives good error rate estimates?
Guyon, I., Makhoul, J., Schwartz, R. M., & Vapnik, V. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655649 · Cited by 156
Garcia-Salicetti, S., Beumier, C., Chollet, G., et al. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44887-x_98 · Cited by 150
A Comparative Evaluation of Fusion Strategies for Multimodal Biometric Verification
Fierrez-Aguilar, J., Ortega-García, J., Garcia-Romero, D., et al. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44887-x_96 · Cited by 103
Self-citationBaron, R., & Réjean Plamondon, B. (1989) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/19.46414 · Cited by 52
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Multimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279
Hafemann, L. G., Sabourin, R., & Oliveira, L. S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.012 · Cited by 391
HMM-based on-line signature verification: Feature extraction and signature modeling
Fierrez, J., Ortega-Garcia, J., Ramos, D., & Gonzalez-Rodriguez, J. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.07.012 · Cited by 309
Self-citationAn improved BioHashing for human authentication
Lumini, A., & Nanni, L. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.05.030 · Cited by 272
Demographic Bias in Biometrics: A Survey on an Emerging Challenge
Drozdowski, P., Rathgeb, C., Dantcheva, A., Damer, N., & Busch, C. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2992344 · Cited by 263
Metadata
- Authors
- Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., & Moro, Q.-I.
- Venue
- IEE Proceedings - Vision, Image, and Signal Processing
- Published
- 2003
- Volume
- 150
- Issue
- 6
- Pages
- 395
- ISSN
- 1350-245X (ISSN-L)
- OpenAlex
- W2053438338
- Cited by
- 757
- References
- 9
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- closed
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