Ensembles of Support Vector Data Description for Active Learning Based Annotation of Affective Corpora

The present work aims primary at developing an approach to detect irregular and spontaneous facial gestures in video sequences. The developed approach should help a system distinguish between neutral facial expressions characterized by the absence of facial gestures and facial events characterized by the presence of observable facial gestures in video sequences. For this purpose, an active learning approach is proposed in order to avoid the task of annotating an entire video sequence before proceeding with the classification. It is well known that the annotation task is hard, expensive and error prone. Each video sequence is segmented into smaller segments that are then to be investigated and annotated based on the absence or presence of facial gestures. The approach consists in first selecting a set of samples classified as uncharacteristic through the majority vote of a committee of support vector data description (SVDD) models generated randomly. The base learner then focuses on the selected outliers and not on the whole annotated corpus. Different query strategies are used to select the most informative samples among the selected outliers. Those samples are then annotated by the user and added to a pool of annotated samples. The latter is subsequently used to train the base learner again before the next iteration can take place. Experiments suggest that the proposed active learning approach performs as well as a system trained on a fully annotated corpus, while dramatically reducing the cost of annotation.