Psychometric Functions Within the Framework of Binary Signal Detection Theory: Coding the Face Identity

  • Petro Gopych
  • Anna Kolot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)


One of standard methods in vision research is measuring the psychometric functions (PFs) that are further analyzed implying the validity of traditional signal detection theory (SDT). This research paradigm contains essential inherent contradiction: in contrast to most empirical PFs the ones predicted by the SDT do not satisfy the Neyman-Pearson objective. The problem may successfully be overcome within the framework of recent binary signal detection theory (BSDT) providing PFs for which the objective required is always achieved. Here, the original BSDT theory for vision is for the first time applied to quantitative description of specific empirical PFs measured in experiments where the coding of facial identity has been studied. By fitting the data, some parameters of BSDT face recognition algorithm were extracted and it was demonstrated that the BSDT supports popular prototype face identification model. Results can be used for developing new high-performance computational methods for face recognition.


neural networks generalization through memory Neyman-Pearson objective face recognition prototype face identification model 


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  1. 1.
    Green, D., Swets, J.: Signal Detection Theory and Psychophysics. Wiley, New York (1966)Google Scholar
  2. 2.
    Macmillan, N.A., Creelman, C.D.: Detection Theory: A User’s Guide, 2nd edn. Lawrence Erlbaum Associates, Mahwah (2005)Google Scholar
  3. 3.
    Leopold, D.A., O’Toole, A.J., Vetter, T., Blanz, V.: Prototype-Referenced Shape Encoding Revealed by High-Level Aftereffects. Nature Neurosci. 4, 89–94 (2001)CrossRefGoogle Scholar
  4. 4.
    Anderson, N.A., Wilson, H.R.: The Nature of Synthetic Face Adaptation. Vision Res. 45, 1815–1828 (2005)CrossRefGoogle Scholar
  5. 5.
    Loffer, G., Yourganov, G., Wilkinson, F., Wilson, H.R.: fMRI Evidence for the Neural Representation of Faces. Nature Neurosci. 8, 1386–1390 (2005)CrossRefGoogle Scholar
  6. 6.
    Rhodes, G., Jeffery, L.: Adaptive Norm-Based Coding of Facial Identity. Vision Res. 46, 2977–2987 (2006)CrossRefGoogle Scholar
  7. 7.
    Ryu, J.-J., Chaudhuri, A.: Representations of Familiar and Unfamiliar Faces as Revealed by Viewpoint-Aftereffects. Vision Res. 46, 4059–4063 (2006)CrossRefGoogle Scholar
  8. 8.
    Leopold, D.A., Bondar, I.V., Giese, M.A.: Norm-Based Face Encoding by Single Neurons in the Monkey Inferotemporal Cortex. Nature 442, 572–575 (2006)CrossRefGoogle Scholar
  9. 9.
    Gopych, P.M.: ROC Curves within the Framework of Neural Network Assembly Memory Model: Some Analytic Results. Int. J. Inf. Theo. Appl. 10, 189–197 (2003)Google Scholar
  10. 10.
    Gopych, P.M.: Sensitivity and Bias within the Binary Signal Detection Theory, BSDT. Int. J. Inf. Theo. Appl. 11, 318–328 (2004)Google Scholar
  11. 11.
    Gopych, P.M.: Neural Network Computations with Negative Triggering Thresholds. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 223–228. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Gopych, P.M.: Generalization by Computation Through Memory. Int. J. Inf. Theo. Appl. 13, 145–157 (2006)Google Scholar
  13. 13.
    Gopych, P.M.: Performance of BSDT Decoding Algorithms Based on Locally Damaged Neural Networks. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 199–206. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Gopych, P.M.: Foundations of the Neural Network Assembly Memory Model. In: Shannon, S. (ed.) Leading-Edge Computer Sciences, pp. 21–84. Nova Science, New York (2006)Google Scholar
  15. 15.
    Gopych, P.M.: Identification of Peaks in Line Spectra Using the Algorithm Imitating the Neural Network Operation. Instr. Exp. Tech. 41, 341–346 (1998)Google Scholar
  16. 16.
    Gopych, P.M., Sorokin, V.I., Sotnikov, V.V.: Human Operator Performance when Identifying Peaks in a Line Spectrum. Instr. Exp. Tech. 35, 446–449 (1992)Google Scholar
  17. 17.
    Summerfield, C., Egner, T., Greene, M., Koechlin, E., Mangels, J., Hirsch, J.: Predictive Codes for Forthcoming Perception in the Frontal Cortex. Science 314, 1311–1314 (2006)CrossRefGoogle Scholar
  18. 18.
    Poggio, T., Bizzi, E.: Generalization in Vision and Motor Control. Nature 431, 768–774 (2004)CrossRefGoogle Scholar
  19. 19.
    Tsao, D.Y., Freiwald, W.A., Tootell, R.B.H., Livingstone, M.S.: A Cortical Region Consisting Entirely of Face-Selective Cells. Science 311, 670–674 (2006)CrossRefGoogle Scholar
  20. 20.
    Burton, A.M., Bruce, V., Hancock, P.J.B.: From Pixels to Peoples: A Model of Familiar Face Recognition. Cog. Science 23, 1–31 (1999)CrossRefGoogle Scholar
  21. 21.
    Jiang, F., Blanz, V., O’Toole, A.J.: The Role of Familiarity in Three-Dimensional View-Transferability of Face Identity Adaptation. Vision Res. 47, 525–531 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Petro Gopych
    • 1
  • Anna Kolot
    • 2
  1. 1.Universal Power Systems USA-Ukraine LLC, 3 Kotsarskaya st., Kharkiv 61012Ukraine
  2. 2.Prof. L.L. Girshman Municipal Clinic no. 14, 5 Oles Gonchar st., Kharkiv 61023Ukraine

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