Detection of Emotional Events utilizing Support Vector Methods in an Active Learning HCI Scenario

In recent years the fields of affective computing and emotion recognition have experienced a steady increase in attention and especially the creation and analysis of multi-modal corpora has been the focus of intense research. Plausible annotation of this data, however is an enormous problem. In detail emotion annotation is very time consuming, cumbersome and sensitive with respect to the annotator. Furthermore emotional reactions are often very sparse in HCI scenarios resulting in a large annotation overhead to gather the interesting moments of a recording, which in turn are highly relevant for powerful features, classifiers and fusion architectures. Active learning techniques provide methods to improve the annotation processes since the annotator is asked to only label the relevant instances of a given dataset. In this work an unsupervised one-class Support Vector Machine is used to build a background model of non-emotional sequences on a novel HCI dataset. The human annotator is iteratively asked to label instances that are not well explained by the background model, which in turn renders them candidates for being interesting events such as emotional reactions that diverge from the norm. The outcome of the active learning procedure is a reduced dataset of only 14% the size of the original dataset that contains most of the significant information, in this case more than 75% of the emotional events.