Face image retrieval using HMMs

The paper introduces a new face recognition system that can be used to index (and thus retrieve) images and videos of a database of faces. New face recognition approaches are needed because, although much progress has been made to identify face taken from different viewpoints, we still cannot robustly identify faces under different illumination conditions, or when the facial expression changes, or when a part of the face is occluded on account of glasses or parts of clothing. When face recognition methods have worked in the past, it was only when all possible "image variations" were learned. Principal components analysis (PCA) and Fisher Discriminant Analysis (FDA) are well-known cases of such methods. We present a different approach to the indexing of face images. Our approach is based on identifying frontal faces and it allows reasonable variability in facial expressions, illumination conditions, and occlusions caused by eye-wear or items of clothing such as scarves. We divide a face image into n different regions, analyze each region with PCA, and then use a Bayesian approach to find the best possible global match between a query image and a database image. The relationships between the n parts is modeled by using Hidden Markov Models (HMMs).