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

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

doi:10.1109/ACSSC.2013.6810420

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

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

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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 language	English (US)
Title of host publication	Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	911-914
Number of pages	4
ISBN (Print)	9781479923908
DOIs	https://doi.org/10.1109/ACSSC.2013.6810420
State	Published - Jan 1 2013
Event	2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 3 2013 → Nov 6 2013

Publication series

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

Other

Other	2013 47th Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	11/3/13 → 11/6/13

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2013.6810420

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). Article 6810420 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810420

@inproceedings{395c1ca36a5940c4a910bd595851665b,

title = "Automated human behavioral analysis framework using facial feature extraction and machine learning",

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.",

author = "Demiyan Smirnov and Sean Banger and Sara Davis and Rajani Muraleedharan and Ravi Ramachandran",

year = "2013",

month = jan,

day = "1",

doi = "10.1109/ACSSC.2013.6810420",

language = "English (US)",

isbn = "9781479923908",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "911--914",

booktitle = "Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers",

address = "United States",

note = "2013 47th Asilomar Conference on Signals, Systems and Computers ; Conference date: 03-11-2013 Through 06-11-2013",

}

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 et al.
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 proceeding › Conference contribution

TY - GEN

T1 - Automated human behavioral analysis framework using facial feature extraction and machine learning

AU - Smirnov, Demiyan

AU - Banger, Sean

AU - Davis, Sara

AU - Muraleedharan, Rajani

AU - Ramachandran, Ravi

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84901265197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901265197&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2013.6810420

DO - 10.1109/ACSSC.2013.6810420

M3 - Conference contribution

AN - SCOPUS:84901265197

SN - 9781479923908

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 911

EP - 914

BT - Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers

PB - IEEE Computer Society

T2 - 2013 47th Asilomar Conference on Signals, Systems and Computers

Y2 - 3 November 2013 through 6 November 2013

ER -

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). doi: 10.1109/ACSSC.2013.6810420

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this