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R. Feraud and O. J. Bernier and J.-E. Viallet and M. Collobert
ABSTRACT
Detecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a new neural network model: the Constrained Generative Model (CGM). Generative, since the goal of the learning process is to evaluate the probability that the model has generated the input data, and constrained since some counterexamples are used to increase the quality of the estimation performed by the model. To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real world application: the indexation of images and videos. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
A Fast and Accurate Face Detector Based on Neural NetworksSite generated on Friday, 06 January 2006