  Affective computing is a field of great interest in many computer vision
applications, including video surveillance, behaviour analysis, and human-robot
interaction. Most of the existing literature has addressed this field by
analysing different sets of face features. However, in the last decade, several
studies have shown how body movements can play a key role even in emotion
recognition. The majority of these experiments on the body are performed by
trained actors whose aim is to simulate emotional reactions. These unnatural
expressions differ from the more challenging genuine emotions, thus
invalidating the obtained results. In this paper, a solution for basic
non-acted emotion recognition based on 3D skeleton and Deep Neural Networks
(DNNs) is provided. The proposed work introduces three majors contributions.
First, unlike the current state-of-the-art in non-acted body affect
recognition, where only static or global body features are considered, in this
work also temporal local movements performed by subjects in each frame are
examined. Second, an original set of global and time-dependent features for
body movement description is provided. Third, to the best of out knowledge,
this is the first attempt to use deep learning methods for non-acted body
affect recognition. Due to the novelty of the topic, only the UCLIC dataset is
currently considered the benchmark for comparative tests. On the latter, the
proposed method outperforms all the competitors.
