Ensemble and Modular Approaches for Face Detection: A Comparison

A new learning model based on autoassociative neural networks is developped and applied to face detection. To extend the de(cid:173) tection ability in orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases. Our purpose is to classify an extracted window x from an image as a face (x E V) or non-face (x EN). The set of all possible windows is E V uN, with V n N 0. Since collecting a representative set of non-face examples is impossible, face detection by a statistical model is a difficult task.