EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 336 (2021) MATEC Web Conf., 336 (2021) 06008 References
Open Access
Issue
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
Article Number 06008
Number of page(s) 7
Section Artificial Recognition and Application
DOI https://doi.org/10.1051/matecconf/202133606008
Published online 15 February 2021
  1. C.G. Zeinstra, D. Meuwly, A.C. Ruifro, R.N. Veldhuis, L.J. Spreeuwers, Forensic face recognition as a means to determine strength of evidence: a survey. Forensic Sci Rev, 30, 1, 21-32 (2018). [Google Scholar]
  2. P. Tome, J. Fierrez, R. Vera‐Rodriguez, J. Ortega‐Garcia, Combination of face regions in forensic scenarios. Journal of forensic sciences, 60, 4, 1046-1051 (2015). [Google Scholar]
  3. Facial Identification Scientific Working Group: Facial comparison overview and methodology guidelines, https://fiswg.org/fiswg_facial_comparison_overview_and_methodology_guidelines_V 1.0_20191025.pdf. [Google Scholar]
  4. M.S. Nixon, P.L. Correia, K. Nasrollahi, T.B. Moeslund, A. Hadid, M. Tistarelli, On soft biometrics. Pattern Recognition Letters, 68, 218-230 (2015). [CrossRef] [Google Scholar]
  5. P. Tome, R. Vera-Rodriguez, J. Fierrez, J. Ortega-Garcia, Facial soft biometric features for forensic face recognition. Forensic science international, 257, 271-284 (2015). [Google Scholar]
  6. M. Jacquet, C. Champod, Automated face recognition in forensic science: Review and perspectives. Forensic Science International, 307, 110124 (2020). [Google Scholar]
  7. A.L. Mölder, I. E. Åström, E. Leitet, Development of a score-to-likelihood ratio model for facial recognition using authentic criminalistic data. In 2020 8th International Workshop on Biometrics and Forensics (IWBF), IEEE, 1-6 (2020). [Google Scholar]
  8. D. Meuwly, D. Ramos, R. Haraksim, A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation. Forensic science international, 276, 142-153 (2017). [Google Scholar]
  9. N. Suki, N. Poh, F.M. Senan, N.A. Zamani, M. Z. A. Darus, On the reproducibility and repeatability of likelihood ratio in forensics: A case study using face biometrics. In 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), 1-8 (2016). [Google Scholar]
  10. A. Macarulla Rodriguez, Z. Geradts, M. Worring, Likelihood Ratios for Deep Neural Networks in Face Comparison. Journal of Forensic Sciences, 65, 4, 1169-1183 (2020). [Google Scholar]
  11. G.S. Morrison, E. Enzinger, Score based procedures for the calculation of forensic likelihood ratios–Scores should take account of both similarity and typicality. Science & Justice, 58, 1, 47-58 (2018). [Google Scholar]
  12. N. Garton, D. Ommen, J. Niemi, A. Carriquiry, Score-based likelihood ratios to evaluate forensic pattern evidence. arXiv preprint arXiv:2002.09470, 1-22 (2020). [Google Scholar]
  13. L. McKenna, S. McDermott, G. O’Donell, ENFSI Guideline for Evaluative Reporting in Forensic Science: Strengthening the evaluation of forensic results across Europe (STEOFRAE). Wiesbaden, Germany: European Network of Forensic Science Institutes, 30-41 (2015). [Google Scholar]
  14. T. Ali, L. Spreeuwers, R. Veldhuis, D. Meuwly, Effect of calibration data on forensic likelihood ratio from a face recognition system. In 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 1-8 (2013). [Google Scholar]
  15. D. Ramos, R.P. Krish, J. Fierrez, D. Meuwly, From biometric scores to forensic likelihood ratios. In Handbook of biometrics for forensic science, Springer, Cham, 305-327, (2017). [Google Scholar]
  16. F.S. Kool, Feature-based models for forensic likelihood ratio calculation: Supporting research for the ENFSI-LR project, (2016). [Google Scholar]
  17. T. Ali, Biometric Score Calibration for Forensic Face Recognition. Ph.D. Thesis Series, Centre for Telematics and Information Technology, 14-336 (2014). [Google Scholar]
  18. A. Dempster, Upper and lower probabilities induced by multivalued mapping. Annals of Mathematical Statistics, 38, 2, 325-339 (1967). [Google Scholar]
  19. G. Shafer, A mathematical theory of evidence. Princeton University Press, 1976 [Google Scholar]
  20. P. Xu, X. Su, S. Mahadevan, C. Li, Y. Deng, A non-parametric method to determine basic probability assignment for classification problems. Applied intelligence, 41, 3, 681-693 (2014). [Google Scholar]
  21. B. Qin, F. Xiao, A non-parametric method to determine basic probability assignment based on kernel density estimation. IEEE Access, 6: 73509-73519 (2018). [Google Scholar]
  22. G.B. Huang, M. Mattar, T. Berg, E. Learned-Miller, Labeled faces in the wild: A database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report, 07-49 (2007). [Google Scholar]
  23. A. Fydanaki, Z. Geradts, Evaluating OpenFace: an open-source automatic facial comparison algorithm for forensics. Forensic sciences research, 3, 3, 202-209 (2018). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.