Development Of a Multisensorial System For Emotions Recognition

Made available in DSpace on 2018-08-02T00:00:40Z (GMT). No. of bitstreams: 1 tese_10810_Hamilton Rivera Flor20171019-95619.pdf: 4725252 bytes, checksum: 16042ed4abfc5b07268db9f41baa2a83 (MD5) Previous issue date: 2017-03-17Automated reading and analysis of human emotion has the potential to be a powerful tool to develop a wide variety of applications, such as human-computer interaction systems, but, at the same time, this is a very difficult issue because the human communication is very complex. Humans employ multiple sensory systems in emotion recognition. At the same way, an emotionally intelligent machine requires multiples sensors to be able to create an affective interaction with users. Thus, this Master thesis proposes the development of a multisensorial system for automatic emotion recognition. The multisensorial system is composed of three sensors, which allowed exploring different emotional aspects, as the eye tracking, using the IR-PCR technique, helped conducting studies about visual social attention; the Kinect, in conjunction with the FACS-AU system technique, allowed developing a tool for facial expression recognition; and the thermal camera, using the FT-RoI technique, was employed for detecting facial thermal variation. When performing the multisensorial integration of the system, it was possible to obtain a more complete and varied analysis of the emotional aspects, allowing evaluate focal attention, valence comprehension, valence expressions, facial expression, valence recognition and arousal recognition. Experiments were performed with sixteen healthy adult volunteers and 105 healthy children volunteers and the results were the developed system, which was able to detect eye gaze, recognize facial expression and estimate the valence and arousal for emotion recognition, This system also presents the potential to analyzed emotions of people by facial features using contactless sensors in semi-structured environments, such as clinics, laboratories, or classrooms. This system also presents the potential to become an embedded tool in robots to endow these machines with an emotional intelligence for a more natural interaction with humans. Keywords: emotion recognition, eye tracking, facial expression, facial thermal variation, integration multisensoria