Abstract
Automatic emotion recognition constitutes one of the great challenges providing new tools for more objective and quicker diagnosis, communication and research. Quick and accurate emotion recognition may increase possibilities of computers, robots, and integrated environments to recognize human emotions, and response accordingly to them a social rules. The purpose of this paper is to investigate the possibility of automated emotion representation, recognition and prediction its state-of-the-art and main directions for further research. We focus on the impact of emotion analysis and state of the arts of multimodal emotion detection. We present existing works, possibilities and existing methods to analyze emotion in text, sound, image, video and physiological signals. We also emphasize the most important features for all available emotion recognition modes. Finally, we present the available platform and outlines the existing projects, which deal with multimodal emotion analysis.
Highlights
Affective Computing (AC) has been a popular area of research for several years
It is very useful to design and develop systems that can measure the emotional state of a person based on, for example, gestures, facial expression, acoustic characteristics and emotions expressed in the text
One of the most challenges in multimodal emotion analysis is to model the interactions between language, visual and acoustic behaviors that change the observation of the expressed emotion
Summary
Many research has been conducted to enable machines detect and understand human affective states, such as emotions, interests and the behavior. It attempts to bridge the communication gap between human users and computers with “soulless” and “emotionless”. The scientific publications of Rosalind Picard (MIT) have introduced a great progress in this field since the nineties [3, 4] He is one of the pioneers of affective computing. The AutoEmotive (MIT Media Lab) is a prototype equipped with sensors and a camera placed on the steering wheel [5] This vehicle measures the level of stress and fatigue of the driver.
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