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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 12328
School of Cognitive Sciences
  Title:   A bayesian framework for face recognition
  Author(s): 
1.  Mohammad Reza Daliri
2.  Morteza Saraf Yazd
  Status:   Published
  Journal: International Journal of Innovative Computing, Information and Control
  Vol.:  8
  Year:  2012
  Pages:   1-13
  Supported by:  IPM
  Abstract:
In this paper, a statistical face recognition scheme proposed by combining the techniques of Bayes? theorem and Parzen estimation applied on various features such as discrete wavelet transform (DWT), Discrete Cosine Transform (DCT) and Principle Component Analysis (PCA). Parzen algorithm estimates the conditional probabilities for each class and according to Bayes? theorem; the class with maximum posterior probability is selected for each test face image. The optimal Gaussian variances for each class have been found by the Genetic Algorithm (GA) optimization. The experiments on the ORL dataset demonstrate that the proposed Parzen based Bayesian classification method with enough DWT features leads, in mean recognition improvement, to 0.2

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