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 maximum 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 face 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 algorithms are evaluated by the Cumulative Rank Curve. I. LECTURE REVIEW 2D face recognition has been a popular and challenging research area since last decade. It arises general interests in computer vision, image analysis, psychology, etc. The problem can be formulated as that: given a set of face images with labelled identity (the gallery) and a set of unlabelled face images (the probe), identify the person in the probe images. Many works have been proposed for this problem till now. Below we briefly introduce the linear space methods, including eigenfaces and fisherfaces, and the method based on Bayesian theory on intrapersonal and extrapersonal classes. These methods are proved to be generally effective for many real applications [4]. A. Linear Methods In 1991, Turk and Pentland [1] proposed “eigenfaces” method. They generate “eigenfaces” from the training face images by principal component analysis (PCA), and classify the new November 11, 2005 DRAFT