Face exemplars selection from video streams for online learning

This paper tackles the problem of online acquisition of exemplars for dynamic updating of classifiers for facial analysis. Most facial analysis systems apply a previously computed classifier to a set of images, or recently to the output of real-time face detection systems. Here we describe an approach to select significant detected faces during interactive sessions in order to learn and modify online, with the initial help of an expert, a classifier for a given task. Preliminary experiments are reported related to gender recognition.