Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision and Applications
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Real-time face detection and tracking for mobile videoconferencing
Real-Time Imaging
Assessing facial beauty through proportion analysis by image processing and supervised learning
International Journal of Human-Computer Studies
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Gradient based method for static facial features localization
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
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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.