A probabilistic framework for perceptual grouping of features for human face detection

Present approaches to human face detection have made several assumptions that restrict their ability to be extended to general imaging conditions. We identify that the key factor in a generic and robust system is that of exploiting a large amount of evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a face detection framework that groups image features into meaningful entities-using perceptual organization, assigns probabilities to each of them, and reinforce there probabilities using Bayesian reasoning techniques. True hypotheses of faces will be reinforced to a high probability. The detection of faces under scale, orientation and viewpoint variations will be examined in a subsequent paper.