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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)

Abstract

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.

Keywords

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

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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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