Abstract
AbstractNon-verbal communication is of paramount importance in person-to-person interaction, as emotions are an integral part of human beings. A sociable robot should therefore display similar abilities as a way to interact seamlessly with the user. This work proposes a model for inference of conveyed emotion in real situations where a human is talking. It is based on the analysis of instantaneous emotion by Kalman filtering and the continuous movement of the emotional state over an Emotional Surface, resulting in evaluations similar to humans in conducted tests. A simulation-optimization heuristic for system tuning is described and allows easy adaptation to various facial expression analysis applications.
Highlights
Person-to-person communication constitutes natural, highly dynamical and multimodal uncertain systems
This paper focuses on emotion recognition based on facial expressions
The model takes emotion detection from video frames as worked by many authors [7,8,22,23]. Any of these software packages for facial expression analysis can be taken as a “raw sensor” from which data to be processed in the proposed model is obtained
Summary
Person-to-person communication constitutes natural, highly dynamical and multimodal uncertain systems. Studies reveal that nonverbal components such as facial expressions, body. This is a revised and extended version of a paper that appeared at ENIA 2011, the Brazilian Meeting on Artificial Intelligence (http://www.dimap.ufrn.br/csbc2011/eventos/enia.php). Multimodal studies have shown that humans correctly recognize the conveyed emotion expressed through speech in about 60 % of interactions. This paper focuses on emotion recognition based on facial expressions. This work discusses a general model for the detection of emotional states and presents a model to detect slow dynamic emotions that constitute the perceived emotional state of the speaker. It is organized as follows: reference material is presented, while Sect. It is organized as follows: reference material is presented in Sect. 2, while Sect. 3 presents the general model, Sect. 4 describes the specific proposed model, the Kalman filtering technique and the heuristics used for model tuning, Sect. 5 describes the proposed experiment and results
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