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article

Fuzzy model of dominance emotions in affective computing

Authors:

Kaveh Bakhtiyari,

Hafizah HusainAuthors Info & Claims

Neural Computing and Applications, Volume 25, Issue 6

Pages 1467 - 1477

https://doi.org/10.1007/s00521-014-1637-6

Published: 01 November 2014 Publication History

Abstract

To date, most of the human emotion recognition systems are intended to sense the emotions and their dominance individually. This paper discusses a fuzzy model for multilevel affective computing based on the dominance dimensional model of emotions. This model can detect any other possible emotions simultaneously at the time of recognition. One hundred and thirty volunteers from various countries with different cultural backgrounds were selected to record their emotional states. These volunteers have been selected from various races and different geographical locations. Twenty-seven different emotions with their strengths in a scale of 5 were questioned through a survey. Recorded emotions were analyzed with the other possible emotions and their levels of dominance to build the fuzzy model. Then this model was integrated into a fuzzy emotion recognition system using three input devices of mouse, keyboard and the touch screen display. Support vector machine classifier detected the other possible emotions of the users along with the directly sensed emotion. The binary system (non-fuzzy) sensed emotions with an incredible accuracy of 93 %. However, it only could sense limited emotions. By integrating this model, the system was able to detect more possible emotions at a time with slightly lower recognition accuracy of 86 %. The recorded false positive rates of this model for four emotions were measured at 16.7 %. The resulted accuracy and its false positive rate are among the top three accurate human emotion recognition (affective computing) systems.

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Cited By

Taverner JVivancos EBotti V(2023)A Multidimensional Culturally Adapted Representation of Emotions for Affective Computational Simulation and RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2020.303058614:1(761-772)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TAFFC.2020.3030586
Srivastava SKumar ASingh APrakash SKumar A(2022)An improved approach towards biometric face recognition using artificial neural networkMultimedia Tools and Applications10.1007/s11042-021-11721-281:6(8471-8497)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-021-11721-2
Wu PLi XShen SHe D(2020)Social media opinion summarization using emotion cognition and convolutional neural networksInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2019.07.00451:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2019.07.004
Show More Cited By

Index Terms

Fuzzy model of dominance emotions in affective computing
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 25, Issue 6

Nov 2014

270 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

Copyright © Copyright © 2014 The Natural Computing Applications Forum.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2014

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Cited By

Taverner JVivancos EBotti V(2023)A Multidimensional Culturally Adapted Representation of Emotions for Affective Computational Simulation and RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2020.303058614:1(761-772)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TAFFC.2020.3030586
Srivastava SKumar ASingh APrakash SKumar A(2022)An improved approach towards biometric face recognition using artificial neural networkMultimedia Tools and Applications10.1007/s11042-021-11721-281:6(8471-8497)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-021-11721-2
Wu PLi XShen SHe D(2020)Social media opinion summarization using emotion cognition and convolutional neural networksInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2019.07.00451:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2019.07.004
Kumar YSahoo G(2017)A two-step artificial bee colony algorithm for clusteringNeural Computing and Applications10.1007/s00521-015-2095-528:3(537-551)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s00521-015-2095-5
van Oosterhout AJumisko-Pyykkö SAlonso MHäkkila JOjala T(2016)Counteract or assist?Proceedings of the 15th International Conference on Mobile and Ubiquitous Multimedia10.1145/3012709.3012739(241-247)Online publication date: 12-Dec-2016
https://dl.acm.org/doi/10.1145/3012709.3012739
Bakhtiyari KTaghavi MHusain H(2015)Hybrid affective computing--keyboard, mouse and touch screenNeural Computing and Applications10.1007/s00521-014-1790-y26:6(1277-1296)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s00521-014-1790-y

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