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References

  1. 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/.
  2. 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/.
  3. 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
  4. 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
  5. 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
  6. 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
  7. Breiman, L. (2001). Random forests, Machine Learning 45(1): 5–32.10.1023/A:1010933404324
  8. 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
  9. Champod, C. and Tistarelli, M. (Eds) (2017). Handbook of Bio-metrics for Forensic Science, Springer, Cham.10.1007/978-3-319-50673-9
  10. 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
  11. 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
  12. 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/.
  13. 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
  14. 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.
  15. Faigman, D.L. (Ed.) (2002). Modern Scientific Evidence: The Law and Science of Expert Testimony, 2nd Edn, West Group, St. Paul.
  16. 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/.
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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.
  22. 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
  23. 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/.
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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.
  30. Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest, RNews 2(3): 18–22.
  31. 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
  32. Mack, B., Roscher, R. and Waske, B. (2014). Can I trust my one-class classification?, Remote Sensing 6(9): 8779–8802.10.3390/rs6098779
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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.
  42. 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
  43. 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
  44. 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
  45. 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
  46. Thompson, W. (2006). Tarnish on the “gold standard”: Recent problems in forensic DNA testing, The Champion 30: 10–16.
  47. 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
  48. Turunen, E. and Dološ, K. (2021). Revealing driver’s natural behavior—A GUHA data mining approach, Mathematics 9(15): 1818.10.3390/math9151818
  49. 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
  50. 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
DOI: https://doi.org/10.34768/amcs-2021-0040 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 587 - 599
Submitted on: Apr 9, 2021
Accepted on: Oct 19, 2021
Published on: Dec 30, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2021 Klara Dološ, Conrad Meyer, Andreas Attenberger, Jessica Steinberger, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.