A unified Bayesian framework for face recognition

This paper introduces a Bayesian framework for face recognition which unifies popular methods such as the eigenfaces and Fisherfaces and can generate two novel probabilistic reasoning models (PRM) with enhanced performance. The Bayesian framework first applies principal component analysis (PCA) for dimensionality reduction with the resulting image representation enjoying noise reduction and enhanced generalization abilities for classification tasks. Following data compression, the Bayes classifier which yields the minimum error when the underlying probability density functions (PDF) are known, carries out the recognition in the reduced PCA subspace using the maximum a posteriori (MAP) rule, which is the optimal criterion for classification because it measures class separability. The PRM models are described within this unified Bayesian framework and shown to yield better performance against both the eigenfaces and Fisherfaces methods