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 factorina generic androbustsystem is thatofexploitinga 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 intomeaningfulentitiesusing perceptual organization, assigns probabilities to each of them, and reinforce these probabilitiesusingBayesian 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.

• Type: Conference
• Year: 1996
• Where: FGR
• Authors: Kin Choong Yow, Roberto Cipolla
• Keywords: Biometrics, Face Detection, Face Detection Framework, FGR 1996, Human Face Detection