The project develop a computational tool integrating facial expressions and biosignals based on shape analysis and bayesian networks for interpreting emotions.
The role of emotions is crucial, not only in everyday life, but especially in the clinical manifestation of several neuropsychiatric disorders and mental health problems.
The key research question is: what is the most effective way to measure emotional states? Actually, an incredible amount of information can be obtained by examining facial expressions and physiological reactivity. These measurement methods are non-invasive, readily available, relatively inexpensive and, without doubts, more objective than self-report questionnaire technique, that, at most and not in real time, can reveal just subjective moods.
This project has been motivated by the need for more objective and quantitative tools allowing to measure and identify emotions in both clinical and non clinical contexts.
Actually, the project aims at integrating, using statistical methods, data derived from the analysis of facial expression, biosignals, questionnaires and patients characteristics, in order to yield significant insight into the mechanism of emotional response, thus deepen theoretical knowledge especially in the field of anxiety and eating disorders.
Therefore, the goal of the project is to realize a non-invasive tool able to simultaneously evaluate psychophysiological response and changes in facial expression, thus identifying significant patterns of affective states.
The ultimate project's aim is to develop a reliable and robust research tool for emotion recognition in patients affected by obsessive compulsive disorder and in aptients affected by anorexia nervosa, generalizable to other clinical populations, allowing to objectively quantify and identify emotional response.
This project was funded by MIUR and the funds are more than € 700.000 (FIRB 2012 project # RBFR12VHR7).