Automated human behavioral analysis framework using facial feature extraction and machine learning

Demiyan Smirnov, Sean Banger, Sara Davis, Rajani Muraleedharan, Ravi Ramachandran

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

1 Citation (Scopus)

Abstract

Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1% of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages911-914
Number of pages4
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

Fingerprint

Learning systems
Feature extraction

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Cite this

Smirnov, D., Banger, S., Davis, S., Muraleedharan, R., & Ramachandran, R. (2013). Automated human behavioral analysis framework using facial feature extraction and machine learning. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 911-914). [6810420] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810420
Smirnov, Demiyan ; Banger, Sean ; Davis, Sara ; Muraleedharan, Rajani ; Ramachandran, Ravi. / Automated human behavioral analysis framework using facial feature extraction and machine learning. Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society, 2013. pp. 911-914 (Conference Record - Asilomar Conference on Signals, Systems and Computers).
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abstract = "Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1{\%} of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.",
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Smirnov, D, Banger, S, Davis, S, Muraleedharan, R & Ramachandran, R 2013, Automated human behavioral analysis framework using facial feature extraction and machine learning. in Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers., 6810420, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 911-914, 2013 47th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 11/3/13. https://doi.org/10.1109/ACSSC.2013.6810420

Automated human behavioral analysis framework using facial feature extraction and machine learning. / Smirnov, Demiyan; Banger, Sean; Davis, Sara; Muraleedharan, Rajani; Ramachandran, Ravi.

Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society, 2013. p. 911-914 6810420 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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

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AB - Emotional intelligence is essential in understanding and predicting human behavior. Although human emotion is best captured using non-intrusive methods, due to factors such as system complexity, computation time and decision response time, the reality of automated behavioral analysis is hindered. In this paper, we propose a framework capable of recognizing emotions of an individual to identify any suspicious behavior. Our research shows 91.1% of emotion classification accuracy for cooperative individuals using facial feature extraction and machine learning techniques, thus outperforming existing state-of-the-art approaches.

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Smirnov D, Banger S, Davis S, Muraleedharan R, Ramachandran R. Automated human behavioral analysis framework using facial feature extraction and machine learning. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society. 2013. p. 911-914. 6810420. (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2013.6810420