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Emotion sensing from head motion capture
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Samanta, Atanu and Guha, Tanaya (2021) Emotion sensing from head motion capture. IEEE Sensors Journal, 21 (4). pp. 5035-5043. doi:10.1109/JSEN.2020.3033431 ISSN 1530-437X.
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WRAP-Emotion-sensing-head-motion-capture-Guha-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1688Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/JSEN.2020.3033431
Abstract
Computational analysis of emotion from verbal and non-verbal behavioral cues is critical for human-centric intelligent systems. Among the non-verbal cues, head motion has received relatively less attention, although its importance has been noted in several research. We propose a new approach for emotion recognition using head motion captured using Motion Capture (MoCap). Our approach is motivated by the well known kinesics-phonetic analogy, which advocates that, analogous to human speech being composed of phonemes, head motion is composed of kinemes i.e., elementary motion units. We discover a set of kinemes from head motion in an unsupervised manner by projecting them onto a learned basis domain and subsequently clustering them. This transforms any head motion to a sequence of kinemes. Next, we learn the temporal latent structures within the kineme sequence pertaining to each emotion. For this purpose, we explore two separate approaches – one using Hidden Markov Model and another using artificial neural network. This class-specific, kineme-based representation of head motion is used to perform emotion recognition on the popular IEMOCAP database. We achieve high recognition accuracy (61.8% for three class) for various emotion recognition tasks using head motion alone. This work adds to our understanding of head motion dynamics, and has applications in emotion analysis and head motion animation and synthesis.
Item Type: | Journal Article | ||||||||
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Subjects: | B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Emotion recognition, Emotion recognition -- Computer programs, Human face recognition (Computer science), Kinematics , Hidden Markov models, Motion | ||||||||
Journal or Publication Title: | IEEE Sensors Journal | ||||||||
Publisher: | Institute of Electrical and Electronic Engineers | ||||||||
ISSN: | 1530-437X | ||||||||
Official Date: | 15 February 2021 | ||||||||
Dates: |
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Volume: | 21 | ||||||||
Number: | 4 | ||||||||
Page Range: | pp. 5035-5043 | ||||||||
DOI: | 10.1109/JSEN.2020.3033431 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 30 October 2020 | ||||||||
Date of first compliant Open Access: | 4 November 2020 |
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