Forensic driver identification considering an unknown suspect

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/. Search in Google Scholar

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/. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Bouchrika, I., Goffredo, M., Carter, J. and Nixon, M. (2011). On using gait in forensic biometrics, Journal of Forensic Sciences 56(4): 882–889, DOI: 10.1111/j.1556-4029.2011.01793.x.10.1111/j.1556-4029.2011.01793.x21554307 Search in Google Scholar

Breiman, L. (2001). Random forests, Machine Learning 45(1): 5–32.10.1023/A:1010933404324 Search in Google Scholar

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 Search in Google Scholar

Champod, C. and Tistarelli, M. (Eds) (2017). Handbook of Bio-metrics for Forensic Science, Springer, Cham.10.1007/978-3-319-50673-9 Search in Google Scholar

Christensen, A.M., Crowder, C.M., Ousley, S.D. and Houck, M.M. (2014). Error and its meaning in forensic science, Journal of Forensic Sciences 59(1): 123–126, DOI: 10.1111/1556-4029.12275.10.1111/1556-4029.1227524111751 Search in Google Scholar

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 Search in Google Scholar

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/. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

Faigman, D.L. (Ed.) (2002). Modern Scientific Evidence: The Law and Science of Expert Testimony, 2nd Edn, West Group, St. Paul. Search in Google Scholar

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/. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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/. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Koehler, J.J. and Liu, S. (2021). Fingerprint error rate on close non-matches, Journal of Forensic Sciences 66(1): 129–134, DOI: 10.1111/1556-4029.14580.10.1111/1556-4029.1458032990979 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest, RNews 2(3): 18–22. Search in Google Scholar

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 Search in Google Scholar

Mack, B., Roscher, R. and Waske, B. (2014). Can I trust my one-class classification?, Remote Sensing 6(9): 8779–8802.10.3390/rs6098779 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Mashao, D.J. and Skosan, M. (2006). Combining classifier decisions for robust speaker identification, Pattern Recognition 39(1): 147–155.10.1016/j.patcog.2005.08.004 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Müller, C. (Ed.) (2007). Speaker Classification I: Fundamentals, Features, and Methods, Lecture Notes in Computer Science, Vol. 4343, Springer, Berlin, DOI: 10.1007/978-3-540-74200-5.10.1007/978-3-540-74200-5 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Saks, M.J. and Koehler, J.J. (2005). The coming paradigm shift in forensic identification science, Science 309(5736): 892–895, DOI: 10.1126/science.1111565.10.1126/science.111156516081727 Search in Google Scholar

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 Search in Google Scholar

Thompson, W. (2006). Tarnish on the “gold standard”: Recent problems in forensic DNA testing, The Champion 30: 10–16. Search in Google Scholar

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 Search in Google Scholar

Turunen, E. and Dološ, K. (2021). Revealing driver’s natural behavior—A GUHA data mining approach, Mathematics 9(15): 1818.10.3390/math9151818 Search in Google Scholar

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 Search in Google Scholar

White, D., Dunn, J.D., Schmid, A.C. and Kemp, R.I. (2015). Error rates in users of automatic face recognition software, PLOS ONE 10(10): e0139827, DOI: 10.1371/journal.pone.0139827.10.1371/journal.pone.0139827460572526465631 Search in Google Scholar