Non-Verbal Communication Analysis in Victim-Offender Mediations.

We present the first VOM system for automatic social signal recognition.Multi-modal audio-RGB-depth features and behavioral indicators are semi-automatically described and recognized.The ground truth is defined by a team of mediators using questionnaires.Different machine learning approaches are used to evaluate social signals in VOM sessions. We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim-Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range 1-5 for the computed social signals.