Towards Automatic Forensic Face Recognition

  • Tauseef Ali
  • Luuk Spreeuwers
  • Raymond Veldhuis
Part of the Communications in Computer and Information Science book series (CCIS, volume 252)


In this paper we present a methodology and experimental results for evidence evaluation in the context of forensic face recognition. In forensic applications, the matching score (hereafter referred to as similarity score) from a biometric system must be represented as a Likelihood Ratio (LR). In our experiments we consider the face recognition system as a ‘black box’ and compute LR from similarity scores. The proposed approach is in accordance with the Bayesian framework where the duty of a forensic scientist is to compute LR from biometric evidence which is then incorporated with prior knowledge of the case by the judge or jury. In our experiments we use a total of 2878 images of 100 subjects from two different databases. Our experimental results prove the feasibility of our approach to reach a LR value given an image of a suspect face and questioned face. In addition, we compare the performance of two biometric face recognition systems in forensic casework.


LR Evidence Similarity score Bayesian framework 


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  1. 1.
    Champod, C., Meuwly, D.: The Inference of Identity in Forensic Speaker Recognition. Speech Commun. 31, 193–203 (2000)CrossRefGoogle Scholar
  2. 2.
    Gonzalez-Rodriguez, J., Fierrez-Aguilar, J., Ortega-Garcia, J.: Forensic Identification Reporting Using Automatic Speaker Recognition Systems. In: Proc. ICASSP (2003)Google Scholar
  3. 3.
    Aitken, C.G.G.: Statistics and Evaluation of Evidence for Forensic Scientists. John Wiley & Sons (1997)Google Scholar
  4. 4.
    Morrison, G.S.: Forensic Voice Comparison. In: Freckelton, I., Selby, H. (eds.) Expert Evidence. Thomson Reuters, Sydney (2010)Google Scholar
  5. 5.
    Buckleton, J.: A Framework for Interpreting Evidence. In: Buckleton, J., Triggs, C.M., Walsh, S.J. (eds.) Forensic DNA Evidence Interpretation, pp. 27–63. CRC, Boca Raton (2005)Google Scholar
  6. 6.
    Peacock, C., Goode, A., Brett, A.: Automatic Forensic Face Recognition from Digital Images. Sci. Justice. 44, 29–34 (2004)CrossRefGoogle Scholar
  7. 7.
    Robertson, B., Vignaux, G.A.: Interpreting Evidence. Wiley, Chichester (1995)Google Scholar
  8. 8.
    Lewis, S.R.: Philosophy of Speaker Identification. Police applications of Speech and Tape Recording Analysis. Proc. of the Institute of Acoustics 6(1), 69–77 (1984)Google Scholar
  9. 9.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)CrossRefzbMATHGoogle Scholar
  10. 10.
    Meuwly, D., Drygajlo, A.: Forensic Speaker Recognition based on a Bayesian Framework and Gaussian Mixture Modeling (GMM). In: Proc. Odysse 2001, pp. 145–150 (2001)Google Scholar
  11. 11.
    Freund, Y., Schapire, R.: A decision-theoretic Generalization of On-line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55, 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)CrossRefGoogle Scholar
  13. 13.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., Li, S.Z.: Ensemble-Based Discriminant Learning with Boosting for Face Recognition. IEEE Trans. Neural Networks 17, 166–178 (2006)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.J.: Robust Real-time Face Detection. Int. J. Comput. Vis. 57, 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Raudys, S.J., Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13, 252–264 (1991)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Kirchberg, K.J., Jesorsky, O., Frischholz, R.W.: Genetic Optimization for Hausdorff-Distance based Face Localization. In: Intl. Workshop on Biometric Authentication, Denmark, pp. 103–111 (2002)Google Scholar
  18. 18.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tauseef Ali
    • 1
  • Luuk Spreeuwers
    • 1
  • Raymond Veldhuis
    • 1
  1. 1.Faculty of Electrical Engineering, Mathematics and Computer ScienceUniversity of TwenteThe Netherlands

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