[
[1] A. A. Ross and A. K. Jain, ”Biometrics, Overview”, Li SZ, Jain AK (Eds.), Encyclopedia of Biometrics, Springer, pp. 289-294, 2015.10.1007/978-1-4899-7488-4_182
]Search in Google Scholar
[
[2] A. K. Jain, A. A. Ross, and K. Nandakumar, Introduction to Biometrics, New York, USA, Springer, 2011.10.1007/978-0-387-77326-1
]Search in Google Scholar
[
[3] G. Doddington, W. Liggett, A. Martin, M. Przybocki, and D. Reynolds, ”Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation”, Tech. Rep, National INST of Standards and Technology Gaithersburg MD, 1998.10.21437/ICSLP.1998-244
]Search in Google Scholar
[
[4] K. O’Connor and S. Elliott, ”Biometric zoo menagerie”, Li SZ, Jain AK (Eds.). Encyclopedia of Biometrics, Springer, pp. 1-4 https://doi.org/10.1007/978-3-642-27733-79146-2, 2014.
]Search in Google Scholar
[
[5] N. Yager and T. Dunstone, ”The biometric menagerie”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 220-230 https://doi.org/10.1109/TPAMI.2008.291, 2010.10.1109/TPAMI.2008.29120075454
]Search in Google Scholar
[
[6] E. Tabassi, ”Image specific error rate: a biometric performance metric”, International Conference on Pattern Recognition, Istanbul, Turkey, pp. 1124-1127, 2010.
]Search in Google Scholar
[
[7] N. Popescu-Bodorin, V. E. Balas, and I. M. Motoc, ”The biometric menagerie - A fuzzy and inconsistent concept”, Balas V, Fodor J, Varkonyi-Koczy A, Dombi J, Jain L. (Eds.). Soft Computing Applications, Advances in Intelligent Systems and Computing, Springer, Berlin, Heidelberg, 195, pp. 27-43 https://doi.org/10.1007/978-3-642-33941-76, 2013.
]Search in Google Scholar
[
[8] N. Poh, S. Bengio, and A. Ross, ”Revisiting Doddingtons zoo: A systematic method to assess user-dependent variabilities”, Proceedings of Second International Workshop on Multimodal User Authentication, Toulouse, France, pp. 1-7, 2006.
]Search in Google Scholar
[
[9] N. Poh and J. Kittler, ”A biometric menagerie index for characterising template/model-specific variation”, Tistarelli M, Nixon MS. (Eds.): International Conference on Biometrics, Advances in Biometrics, LNCS 5558, pp. 816-827 https://doi.org/10.1007/978-3-642-01793-383, 2009.
]Search in Google Scholar
[
[10] N. Houmani and S. Garcia-Salicetti, ”On hunting animals of the biometric menagerie for online signature”, PLoS ONE, vol. 11, no. 4, pp. e0151691 https://doi.org/10.1371/journal.pone.0151691, 2016.10.1371/journal.pone.0151691482439727054836
]Search in Google Scholar
[
[11] K. Sundararajan, T. J. Neal, and D. L. Woodard, ”Style signatures to combat biometric menagerie in stylometry”, International Conference on Biometrics, Gold Coast, QLD, Australia, pp. 263-269, 2018.10.1109/ICB2018.2018.00047
]Search in Google Scholar
[
[12] A. Ross, A. Rattani, and M. Tistarelli, ”Exploiting the Doddington zoo effect in biometric fusion”, Proceedings of 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, Washington DC, USA, pp. 1-7, 2009.
]Search in Google Scholar
[
[13] A. Mhenni, E. Cherrier, C. Rosenberger, N. Essoukri, and B. Amara, ”Analysis of Doddington zoo classification for user dependent template update: Application to keystroke dynamics recognition”, Future Generation Computer Systems, 97, pp. 210-218 https://doi.org/10.1016/j.future.2019.02.039, 2019.10.1016/j.future.2019.02.039
]Search in Google Scholar
[
[14] D. Migdal, I. Magotti, and C. Rosenberger, ”Classifying biometric systems users among the Doddington zoo: Application to keystroke dynamics”, Proceedings of the 18th International Conference on Security and Cryptography, pp. 747-753, 2021.10.5220/0010577500002998
]Search in Google Scholar
[
[15] M. N. Teli, J. R. Beveridge, P. J. Phillips, G. H. Givens, D. S. Bolme, and B. A. Draper, ”Biometric zoos: Theory and experimental evidence”, International Joint Conference on Biometrics, Washington, DC, USA, pp. 1-8, 2011.10.1109/IJCB.2011.6117479
]Search in Google Scholar
[
[16] M. Balazia, S. L. Happy, F. Bremond, and A. Dantcheva, ”How unique is a face: An investigative study”, 25th International Conference on Pattern Recognition, Milan, Italy, pp. 7066-7071, 2020.
]Search in Google Scholar
[
[17] S. Douhou and J. R. Magnus, ”The reliability of user authentication through keystroke dynamics”, Statistica Neerlandica, vol. 63, no. 4, pp. 432-449 https://doi.org/10.1111/j.1467-9574.2009.00434.x, 2009.10.1111/j.1467-9574.2009.00434.x
]Search in Google Scholar
[
[18] S. Banerjee, Z. Syed, N. Bartlow, and B. Cukic, ”Keystroke recognition”, Li SZ, Jain AK (Eds.). Encyclopedia of Biometrics, Springer, pp. 1067-1073 https://doi.org/10.1007/9781-4899-7488-4205, 2015.
]Search in Google Scholar
[
[19] R. S. Gaines, W. Lisowski, S. J. Press, and N. Shapiro, ”Authentication by keystroke timing: Some preliminary results”, Technical Report, Rand Corporation, Santa Monica, CA, USA, 1980.
]Search in Google Scholar
[
[20] R. Joyce and G. Gupta, ”Identity authentication based on keystroke latencies”, Communications of the ACM, vol. 33, no. 2, pp. 168-176 https://doi.org/10.1145/75577.75582, 1990.10.1145/75577.75582
]Search in Google Scholar
[
[21] F. Monrose and A. D. Rubin, ”Keystroke dynamics as a biometric for authentication”, Future Generation Computer Systems, vol. 16, no. 4, pp. 351-359 https://doi.org/10.1016/S0167-739X(99)00059-X, 2000.10.1016/S0167-739X(99)00059-X
]Search in Google Scholar
[
[22] M. Karnan, M. Akila, and N. Krishnaraj, ”Biometric personal authentication using keystroke dynamics: A review”, Applied Soft Computing, vol. 11, no. 2, pp. 1565-1573 https://doi.org/10.1016/j.asoc.2010.08.003, 2011.10.1016/j.asoc.2010.08.003
]Search in Google Scholar
[
[23] R. Giot, M. El-Abed, and C. Rosenberger, ”Keystroke dynamics authentication”, Yang J. (Ed.). Biometrics. InTechOpen, pp. 157-182 https://doi.org/10.5772/17064, 2011.10.5772/17064
]Search in Google Scholar
[
[24] H. C. Chang, J. Li, C. S. Wu, and M. Stamp, ”Machine learning and deep learning for fixed-text keystroke dynamics”, Stamp, M., Aaron Visaggio, C., Mercaldo, F., Di Troia, F. (eds) Artificial Intelligence for Cybersecurity. Advances in Information Security, 54, Springer, Cham, pp. 309-329 https://doi.org/10.1007/978-3-030-97087-113, 2022.
]Search in Google Scholar
[
[25] A. Acien, A. Morales, J. V. Monaco, R. Vera-Rodriguez and J. Fierrez, ”TypeNet: Deep learning keystroke biometrics”, IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 4, no. 1, pp. 57-70 https://doi.org/10.1109/TBIOM.2021.3112540, 2022.10.1109/TBIOM.2021.3112540
]Search in Google Scholar
[
[26] R. Giot, M. El-Abed, and C. Rosenberger, ”GREYC Keystroke: A benchmark for keystroke dynamics biometric systems”, IEEE International Conference on Biometrics: Theory, Applications and Systems, Washington, DC, USA, pp. 1-6, 2009.10.1109/BTAS.2009.5339051
]Search in Google Scholar
[
[27] K. S. Killourhy and R. A. Maxion, ”Comparing anomaly detectors for keystroke dynamics”, Proceedings of the 39th Annual International Conference on Dependable Systems and Networks, Estoril, Lisbon, Portugal, pp. 125-134, 2009.10.1109/DSN.2009.5270346
]Search in Google Scholar
[
[28] M. E. Özbek, ”Classification performance improvement of keystroke data”, Innovations in Intelligent Systems and Applications Conference, vol. no. ASYU, pp. 1-4 https://doi.org/10.1109/ASYU48272.2019.8946366, 2019.10.1109/ASYU48272.2019.8946366
]Search in Google Scholar
