Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks

Technologies

Evangelos Misirlis¹  Antonios Gasteratos¹  Konstantinos Tsintotas¹  Ioannis Kansizoglou¹ 

[1] Department of Production and Management Engineering, Democritus University of Thrace, GR-671 32 Xanthi, Greece;

关键词: human-centered computing;    affective computing;    continuous emotion recognition;    behavior modeling;   

DOI  :  10.3390/technologies10030059

来源: DOAJ

【 摘 要 】

One’s internal state is mainly communicated through nonverbal cues, such as facial expressions, gestures and tone of voice, which in turn shape the corresponding emotional state. Hence, emotions can be effectively used, in the long term, to form an opinion of an individual’s overall personality. The latter can be capitalized on in many human–robot interaction (HRI) scenarios, such as in the case of an assisted-living robotic platform, where a human’s mood may entail the adaptation of a robot’s actions. To that end, we introduce a novel approach that gradually maps and learns the personality of a human, by conceiving and tracking the individual’s emotional variations throughout their interaction. The proposed system extracts the facial landmarks of the subject, which are used to train a suitably designed deep recurrent neural network architecture. The above architecture is responsible for estimating the two continuous coefficients of emotion, i.e., arousal and valence, following the broadly known Russell’s model. Finally, a user-friendly dashboard is created, presenting both the momentary and the long-term fluctuations of a subject’s emotional state. Therefore, we propose a handy tool for HRI scenarios, where robot’s activity adaptation is needed for enhanced interaction performance and safety.

【 授权许可】

Unknown