A fast and accurate face detector for indexation of face images

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 generative neural network model: the constrained generative model (CGM). 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 us to apply this detector to a real-world application: the indexation of face images on the WWW.