Paper
17 January 2005 Multimodal approaches for emotion recognition: a survey
Author Affiliations +
Proceedings Volume 5670, Internet Imaging VI; (2005) https://doi.org/10.1117/12.600746
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
Abstract
Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Nicu Sebe, Ira Cohen, Theo Gevers, and Thomas S. Huang "Multimodal approaches for emotion recognition: a survey", Proc. SPIE 5670, Internet Imaging VI, (17 January 2005); https://doi.org/10.1117/12.600746
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Cited by 109 scholarly publications.
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KEYWORDS
Computing systems

Video

Data modeling

Facial recognition systems

Human-computer interaction

Optical flow

Detection and tracking algorithms

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