An infant facial expression recognition system based on moment feature extraction

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

4 Citations (Scopus)

Abstract

This paper presents a vision-based infant surveillance system utilizing infant facial expression recognition software. In this study, the video camera is set above the crib to capture the infant expression sequences, which are then sent to the surveillance system. The infant face region is segmented based on the skin colour information. Three types of moments, namely Hu, R, and Zernike are then calculated based on the information available from the infant face regions. Since each type of moment in turn contains several different moments, given a single fifteen-frame sequence, the correlation coefficients between two moments of the same type can form the attribute vector of facial expressions. Fifteen infant facial expression classes have been defined in this study. Three decision trees corresponding to each type of moment have been constructed in order to classify these facial expressions. The experimental results show that the proposed method is robust and efficient. The properties of the different types of moments have also been analyzed and discussed.

Original languageEnglish
Title of host publicationVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages313-318
Number of pages6
Publication statusPublished - 2010 Sep 10
Event5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 - Angers, France
Duration: 2010 May 172010 May 21

Publication series

NameVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume2

Other

Other5th International Conference on Computer Vision Theory and Applications, VISAPP 2010
CountryFrance
CityAngers
Period10/5/1710/5/21

Fingerprint

Video cameras
Decision trees
Feature extraction
Skin
Color

Keywords

  • Correlation coefficient
  • Decision tree
  • Facial expression recognition
  • Moment

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Fang, C-Y., Lin, H. W., & Chen, S-W. (2010). An infant facial expression recognition system based on moment feature extraction. In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 313-318). (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 2).

An infant facial expression recognition system based on moment feature extraction. / Fang, Chiung-Yao; Lin, H. W.; Chen, Sei-Wang.

VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications. 2010. p. 313-318 (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 2).

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

Fang, C-Y, Lin, HW & Chen, S-W 2010, An infant facial expression recognition system based on moment feature extraction. in VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications. VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications, vol. 2, pp. 313-318, 5th International Conference on Computer Vision Theory and Applications, VISAPP 2010, Angers, France, 10/5/17.
Fang C-Y, Lin HW, Chen S-W. An infant facial expression recognition system based on moment feature extraction. In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications. 2010. p. 313-318. (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications).
Fang, Chiung-Yao ; Lin, H. W. ; Chen, Sei-Wang. / An infant facial expression recognition system based on moment feature extraction. VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications. 2010. pp. 313-318 (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications).
@inproceedings{a8f20960b1d340359e6e1b698e8cce19,
title = "An infant facial expression recognition system based on moment feature extraction",
abstract = "This paper presents a vision-based infant surveillance system utilizing infant facial expression recognition software. In this study, the video camera is set above the crib to capture the infant expression sequences, which are then sent to the surveillance system. The infant face region is segmented based on the skin colour information. Three types of moments, namely Hu, R, and Zernike are then calculated based on the information available from the infant face regions. Since each type of moment in turn contains several different moments, given a single fifteen-frame sequence, the correlation coefficients between two moments of the same type can form the attribute vector of facial expressions. Fifteen infant facial expression classes have been defined in this study. Three decision trees corresponding to each type of moment have been constructed in order to classify these facial expressions. The experimental results show that the proposed method is robust and efficient. The properties of the different types of moments have also been analyzed and discussed.",
keywords = "Correlation coefficient, Decision tree, Facial expression recognition, Moment",
author = "Chiung-Yao Fang and Lin, {H. W.} and Sei-Wang Chen",
year = "2010",
month = "9",
day = "10",
language = "English",
isbn = "9789896740283",
series = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",
pages = "313--318",
booktitle = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",

}

TY - GEN

T1 - An infant facial expression recognition system based on moment feature extraction

AU - Fang, Chiung-Yao

AU - Lin, H. W.

AU - Chen, Sei-Wang

PY - 2010/9/10

Y1 - 2010/9/10

N2 - This paper presents a vision-based infant surveillance system utilizing infant facial expression recognition software. In this study, the video camera is set above the crib to capture the infant expression sequences, which are then sent to the surveillance system. The infant face region is segmented based on the skin colour information. Three types of moments, namely Hu, R, and Zernike are then calculated based on the information available from the infant face regions. Since each type of moment in turn contains several different moments, given a single fifteen-frame sequence, the correlation coefficients between two moments of the same type can form the attribute vector of facial expressions. Fifteen infant facial expression classes have been defined in this study. Three decision trees corresponding to each type of moment have been constructed in order to classify these facial expressions. The experimental results show that the proposed method is robust and efficient. The properties of the different types of moments have also been analyzed and discussed.

AB - This paper presents a vision-based infant surveillance system utilizing infant facial expression recognition software. In this study, the video camera is set above the crib to capture the infant expression sequences, which are then sent to the surveillance system. The infant face region is segmented based on the skin colour information. Three types of moments, namely Hu, R, and Zernike are then calculated based on the information available from the infant face regions. Since each type of moment in turn contains several different moments, given a single fifteen-frame sequence, the correlation coefficients between two moments of the same type can form the attribute vector of facial expressions. Fifteen infant facial expression classes have been defined in this study. Three decision trees corresponding to each type of moment have been constructed in order to classify these facial expressions. The experimental results show that the proposed method is robust and efficient. The properties of the different types of moments have also been analyzed and discussed.

KW - Correlation coefficient

KW - Decision tree

KW - Facial expression recognition

KW - Moment

UR - http://www.scopus.com/inward/record.url?scp=77956298323&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956298323&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:77956298323

SN - 9789896740283

T3 - VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications

SP - 313

EP - 318

BT - VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications

ER -