A Bayesian Framework for Face Recognition
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% in comparison with Support Vector Machine (SVM) and 5.6% in comparison with K-Nearest Neighbour (KNN) classifier.