SCALE AND ORIENTATION INVARIANCE IN HUMAN FACE DETECTION
Kin Choong Yow and Roberto Cipolla
October 1996
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 these 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.
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