Acien, A., Morales, A., Vera-Rodriguez, R., Fierrez, J. and Monaco, J.V. (2020). TypeNet: Scaling up keystroke biometrics, International Joint Conference on Biometrics (IJCB), Houston, USA, pp. 1–7, https://ieeexplore.ieee.org/document/9304908/.
Antal, M. and Szabo, L.Z. (2015). An evaluation of one-class and two-class classification algorithms for keystroke dynamics authentication on mobile devices, 20th International Conference on Control Systems and Computer Science, Bucharest, Romania, pp. 343–350, http://ieeexplore.ieee.org/document/7168452/.
Benzaoui, A., Hadid, A. and Boukrouche, A. (2014). Ear biometric recognition using local texture descriptors, Journal of Electronic Imaging 23(5): 053008, DOI:10.1117/1.JEI.23.5.053008.10.1117/1.JEI.23.5.053008
Bergamini, C., Oliveira, L., Koerich, A. and Sabourin, R. (2009). Combining different biometric traits with one-class classification, Signal Processing 89(11): 2117–2127.10.1016/j.sigpro.2009.04.043
Bernardi, M.L., Cimitile, M., Martinelli, F. and Mercaldo, F. (2018). Driver and path detection through time-series classification, Journal of Advanced Transportation (3): 1–20.10.1155/2018/1758731
Carfora, M.F., Martinelli, F., Mercaldo, F., Nardone, V., Orlando, A., Santone, A. and Vaglini, G. (2019). A “pay-how-you-drive” car insurance approach through cluster analysis, Soft Computing 23(9): 2863–2875, DOI: 10.1007/s00500-018-3274-y.10.1007/s00500-018-3274-y
Dessimoz, D. and Champod, C. (2008). Linkages between Biometrics and Forensic Science, in A.K. Jain et al. (Eds), Handbook of Biometrics, Springer, New York, pp. 425–459.10.1007/978-0-387-71041-9_21
D’Lima, N. and Mittal, J. (2015). Password authentication using Keystroke Biometrics, 2015 International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, India, pp. 1–6, http://ieeexplore.ieee.org/document/7045681/.
Dološ, K., Meyer, C., Attenberger, A. and Steinberger, J. (2020). Driver identification using in-vehicle digital data in the forensic context of a hit and run accident, Forensic Science International: Digital Investigation 35: 301090.10.1016/j.fsidi.2020.301090
Eude, T. and Chang, C. (2018). One-class SVM for biometric authentication by keystroke dynamics for remote evaluation, Computational Intelligence 34(1): 145–160, DOI:/10.1111/coin.12122.
Ge, Z., Iyer, A.N., Cheluvaraja, S., Sundaram, R. and Ganapathiraju, A. (2017). Neural network based speaker classification and verification systems with enhanced features, Intelligent Systems Conference (IntelliSys), London, UK, pp. 1089–1094, http://ieeexplore.ieee.org/document/8324265/.
Glymour, C., Madigan, D., Pregibon, D. and Smyth, P. (1997). Statistical themes and lessons for data mining, Data Mining and Knowledge Discovery 1(1): 11–28.10.1023/A:1009773905005
Gross, S.R., O’Brien, B., Hu, C. and Kennedy, E. H. (2014). Rate of false conviction of criminal defendants who are sentenced to death, Proceedings of the National Academy of Sciences 111(20): 7230–7235, DOI: 10.1073/pnas.1306417111.10.1073/pnas.1306417111403418624778209
Gupta, S., Buriro, A. and Crispo, B. (2019). DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms, Computers & Security 83: 122–139.10.1016/j.cose.2019.01.007
Haber, L. and Haber, R.N. (2004). Error rates for human latent fingerprint examiners, in N. Ratha and R. Bolle (Eds), Automatic Fingerprint Recognition Systems, Springer, New York, pp. 339–360, DOI: 10.1007/0-387-21685-5_17.10.1007/0-387-21685-5_17
Helm, P. and Hagendorff, T. (2021). Beyond the prediction paradigm: Challenges for machine learning in the struggle against organized crime, Law & Contemporary Problems 84(3): 1–17.
Houck, M.M. and Budowle, B. (2002). Correlation of microscopic and mitochondrial DNA hair comparisons, Journal of Forensic Sciences 47(5): 964–967.10.1520/JFS15515J
Ikuesan, A.R. and Venter, H.S. (2017). Digital forensic readiness framework based on behavioral-biometrics for user attribution, IEEE Conference on Application, Information and Network Security (AINS), Miri, Malaysia, pp. 54–59, http://ieeexplore.ieee.org/document/8270424/.
Khan, S.S. and Madden, M.G. (2010). A survey of recent trends in one class classification, in L. Coyle and J. Freyne (Eds), Artificial Intelligence and Cognitive Science, Springer, Berlin, pp. 188–197, DOI: 10.1007/978-3-642-17080-5_21.10.1007/978-3-642-17080-5_21
Kloosterman, A., Sjerps, M. and Quak, A. (2014). Error rates in forensic DNA analysis: Definition, numbers, impact and communication, Forensic Science International: Genetics 12: 77–85.10.1016/j.fsigen.2014.04.01424905336
Koenig, B.E. (1986). Spectrographic voice identification: A forensic survey, Journal of the Acoustical Society of America 79(6): 2088–2090, DOI: 10.1121/1.393170.10.1121/1.3931703722616
Kupin, A., Moeller, B., Jiang, Y., Banerjee, N.K. and Banerjee, S. (2019). Task-driven biometric authentication of users in virtual reality (VR) environments, in I. Kompatsiaris et al. (Eds), MultiMedia Modeling, Springer, Cham, pp. 55–67, DOI: 10.1007/978-3-030-05710-7_5.10.1007/978-3-030-05710-7_5
Kwak, B.I., Woo, J. and Kim, H.K. (2017). Know your master: Driver profiling-based anti-theft method, arXiv 1704.05223, http://arxiv.org/abs/1704.05223.
Lieberman, J.D., Carrell, C.A., Miethe, T.D. and Krauss, D.A. (2008). Gold versus platinum: Do jurors recognize the superiority and limitations of DNA evidence compared to other types of forensic evidence?, Psychology, Public Policy, and Law 14(1): 27–62, DOI: 10.1037/1076-8971.14.1.27.10.1037/1076-8971.14.1.27
Mack, B. and Waske, B. (2017). In-depth comparisons of MaxEnt, biased SVM and one-class SVM for one-class classification of remote sensing data, Remote Sensing Letters 8(3): 290–299, DOI: 10.1080/2150704X.2016.1265689.10.1080/2150704X.2016.1265689
Martinelli, F., Mercaldo, F., Orlando, A., Nardone, V., Santone, A. and Sangaiah, A.K. (2020). Human behavior characterization for driving style recognition in vehicle system, Computers & Electrical Engineering 83, Article 102504, DOI: 10.1016/j.compeleceng.2017.12.050.10.1016/j.compeleceng.2017.12.050
Mordini, E. (2017). Ethics and policy of forensic biometrics, in M. Tistarelli and C. Champod (Eds), Handbook of Biometrics for Forensic Science, Springer, Cham, pp. 353–365, DOI: 10.1007/978-3-319-50673-9_16.10.1007/978-3-319-50673-9_16
Märkel, U. and Dološ, K. (2017). Tree species site suitability as a combination of occurrence probability and growth and derivation of priority regions for climate change adaptation, Forests 8(6): 181.10.3390/f8060181
Oshiro, T.M., Perez, P.S. and Baranauskas, J.A. (2012). How many trees in a random forest?, in D. Hutchison et al. (Eds), Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin, pp. 154–168, DOI: 10.1007/978-3-642-31537-4_13.10.1007/978-3-642-31537-4_13
Page, M., Taylor, J. and Blenkin, M. (2011). Forensic identification science evidence since Daubert: Part II-Judicial reasoning in decisions to exclude forensic identification evidence on grounds of reliability: Identification evidence since Daubert (II), Journal of Forensic Sciences 56(4): 913–917, DOI: 10.1111/j.1556-4029.2011.01776.x.10.1111/j.1556-4029.2011.01776.x21729081
Remeli, M., Lestyan, S., Acs, G. and Biczok, G. (2019). Automatic driver identification from in-vehicle network logs, arXiv 1911.09508, http://arxiv.org/abs/1911.09508.
Reynolds, D. (1994). Experimental evaluation of features for robust speaker identification, IEEE Transactions on Speech and Audio Processing 2(4): 639–643.10.1109/89.326623
Ross, A., Banerjee, S. and Chowdhury, A. (2020). Security in smart cities: A brief review of digital forensic schemes for biometric data, Pattern Recognition Letters 138: 346–354, DOI: 10.1016/j.patrec.2020.07.009.10.1016/j.patrec.2020.07.009
Stenzel, S., Fassnacht, F.E., Mack, B. and Schmidtlein, S. (2017). Identification of high nature value grassland with remote sensing and minimal field data, Ecological Indicators 74: 28–38.10.1016/j.ecolind.2016.11.005
Tirumala, S.S., Shahamiri, S.R., Garhwal, A.S. and Wang, R. (2017). Speaker identification features extraction methods: A systematic review, Expert Systems with Applications 90: 250–271.10.1016/j.eswa.2017.08.015
Wakita, T., Ozawa, K., Miyajima, C., Igarashi, K., Itou, K., Takeda, K. and Itakura, F. (2005). Driver identification using driving behavior signals, IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, pp. 907–912, http://ieeexplore.ieee.org/document/1520171/.10.4271/2005-08-0569