A real time classifier for emotion and stress recognition in a vehicle driver

M. Paschero, G. Del Vescovo, L. Benucci, A. Rizzi, Marco Santello, G. Fabbri, F. M Frattale Mascioli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Recently there is a great interest in artificial systems able to understand and recognize human emotions. In this paper an Emotion Recognition System based on classical neural networks and neuro-fuzzy classifiers is proposed. Emotion recognition is performed in real time starting from a video stream acquired by a common webcam monitoring the user's face. Neurofuzzy classifiers, in comparison with Multi Layer Perceptron trained by EBP algorithm, show very short training times, allowing applications with easy and automated set up procedures, to be used in a wide range of applications, from entertainment to safety. The algorithm yields very interesting performances and can be adopted to recognize emotions as well as possible pathological conditions of the individual to be monitored.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE International Symposium on Industrial Electronics, ISIE 2012
Pages1690-1695
Number of pages6
DOIs
StatePublished - Aug 15 2012
Event21st IEEE International Symposium on Industrial Electronics, ISIE 2012 - Hangzhou, China
Duration: May 28 2012May 31 2012

Publication series

NameIEEE International Symposium on Industrial Electronics

Other

Other21st IEEE International Symposium on Industrial Electronics, ISIE 2012
CountryChina
CityHangzhou
Period5/28/125/31/12

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Paschero, M., Del Vescovo, G., Benucci, L., Rizzi, A., Santello, M., Fabbri, G., & Mascioli, F. M. F. (2012). A real time classifier for emotion and stress recognition in a vehicle driver. In Proceedings - 2012 IEEE International Symposium on Industrial Electronics, ISIE 2012 (pp. 1690-1695). [6237345] (IEEE International Symposium on Industrial Electronics). https://doi.org/10.1109/ISIE.2012.6237345