Psychometric Functions Within the Framework of Binary Signal Detection Theory: Coding the Face Identity
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.
Keywordsneural networks generalization through memory Neyman-Pearson objective face recognition prototype face identification model
Unable to display preview. Download preview PDF.
- 1.Green, D., Swets, J.: Signal Detection Theory and Psychophysics. Wiley, New York (1966)Google Scholar
- 2.Macmillan, N.A., Creelman, C.D.: Detection Theory: A User’s Guide, 2nd edn. Lawrence Erlbaum Associates, Mahwah (2005)Google Scholar
- 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.Gopych, P.M.: Sensitivity and Bias within the Binary Signal Detection Theory, BSDT. Int. J. Inf. Theo. Appl. 11, 318–328 (2004)Google Scholar
- 12.Gopych, P.M.: Generalization by Computation Through Memory. Int. J. Inf. Theo. Appl. 13, 145–157 (2006)Google Scholar
- 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.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.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