Fuzzy emotion recognition model for video sequences

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Standard

Fuzzy emotion recognition model for video sequences. / Oussalah, M.; Wang, S.

Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on . 2012. p. 127-132.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Harvard

Oussalah, M & Wang, S 2012, Fuzzy emotion recognition model for video sequences. in Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on . pp. 127-132, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), United Kingdom, 15/10/12. https://doi.org/10.1109/IPTA.2012.6469574

APA

Oussalah, M., & Wang, S. (2012). Fuzzy emotion recognition model for video sequences. In Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on (pp. 127-132) https://doi.org/10.1109/IPTA.2012.6469574

Vancouver

Oussalah M, Wang S. Fuzzy emotion recognition model for video sequences. In Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on . 2012. p. 127-132 https://doi.org/10.1109/IPTA.2012.6469574

Author

Oussalah, M. ; Wang, S. / Fuzzy emotion recognition model for video sequences. Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on . 2012. pp. 127-132

Bibtex

@inbook{d0ce4766697846c5885940cd870df1f4,
title = "Fuzzy emotion recognition model for video sequences",
abstract = "Automatic facial expression recognition from video clips is a challenging task due to computational complexity, limitations of image analysis and subjectivity. This paper advocates a fuzzy based approach for emotion classification. On the other hand, several proposals have been put forward to enhance the pre-processing stage prior to the classification. This includes a combination of a boundary elliptical model for skin detection, adaptive thresholding, principal component analysis and use of cam-shift for face tracking. The performances of the developed system have been evaluated using TFEID and video clips and compared with Bayes' classifier.",
author = "M. Oussalah and S. Wang",
year = "2012",
month = oct,
doi = "10.1109/IPTA.2012.6469574",
language = "English",
pages = "127--132",
booktitle = "Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on",
note = "2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA) ; Conference date: 15-10-2012 Through 18-10-2012",

}

RIS

TY - CHAP

T1 - Fuzzy emotion recognition model for video sequences

AU - Oussalah, M.

AU - Wang, S.

PY - 2012/10

Y1 - 2012/10

N2 - Automatic facial expression recognition from video clips is a challenging task due to computational complexity, limitations of image analysis and subjectivity. This paper advocates a fuzzy based approach for emotion classification. On the other hand, several proposals have been put forward to enhance the pre-processing stage prior to the classification. This includes a combination of a boundary elliptical model for skin detection, adaptive thresholding, principal component analysis and use of cam-shift for face tracking. The performances of the developed system have been evaluated using TFEID and video clips and compared with Bayes' classifier.

AB - Automatic facial expression recognition from video clips is a challenging task due to computational complexity, limitations of image analysis and subjectivity. This paper advocates a fuzzy based approach for emotion classification. On the other hand, several proposals have been put forward to enhance the pre-processing stage prior to the classification. This includes a combination of a boundary elliptical model for skin detection, adaptive thresholding, principal component analysis and use of cam-shift for face tracking. The performances of the developed system have been evaluated using TFEID and video clips and compared with Bayes' classifier.

U2 - 10.1109/IPTA.2012.6469574

DO - 10.1109/IPTA.2012.6469574

M3 - Chapter (peer-reviewed)

SP - 127

EP - 132

BT - Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on

T2 - 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)

Y2 - 15 October 2012 through 18 October 2012

ER -