Biometrics Project: Bayesian Face Recognition

This project is to implement a 2D face recognition algorithm proposed in (2), which models the density of intrapersonal and extrapersonal face space separately with a single Gaussian for each, and thus uses Bayesian theory to do classification. It includes both m aximum a posteriori (MAP) and maximum likelihood (ML) decision. Besides, we will try two improvements: one is to use Gaussian Mixture Model for density modelling since there will be multiple modes in intrapersonal face space, and the other one is to use Gabor feature jets instead of pixel intensity in fac e representation. The traditional eigenfaces (1) method is also implemented as a base line for comparison. The experiments will be carried on the ORL database containing 40 subjects and each one has 10 images under different lighting, pose, facial expression, and facial details. All the recognition algori thms are evaluated by the Cumulative Rank Curve.