University of Hertfordshire

Recognizing facial expressions: Computational models and humans

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

Standard

Recognizing facial expressions : Computational models and humans. / Shenoy, Aruna; Davey, N.; Frank, Ray.

2013 13th UK Workshop on Computational Intelligence, UKCI 2013. IEEE, 2013. p. 191-198 6651305.

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

Harvard

Shenoy, A, Davey, N & Frank, R 2013, Recognizing facial expressions: Computational models and humans. in 2013 13th UK Workshop on Computational Intelligence, UKCI 2013., 6651305, IEEE, pp. 191-198, 2013 13th UK Workshop on Computational Intelligence, UKCI 2013, Guildford, Surrey, United Kingdom, 9/09/13. https://doi.org/10.1109/UKCI.2013.6651305

APA

Shenoy, A., Davey, N., & Frank, R. (2013). Recognizing facial expressions: Computational models and humans. In 2013 13th UK Workshop on Computational Intelligence, UKCI 2013 (pp. 191-198). [6651305] IEEE. https://doi.org/10.1109/UKCI.2013.6651305

Vancouver

Shenoy A, Davey N, Frank R. Recognizing facial expressions: Computational models and humans. In 2013 13th UK Workshop on Computational Intelligence, UKCI 2013. IEEE. 2013. p. 191-198. 6651305 https://doi.org/10.1109/UKCI.2013.6651305

Author

Shenoy, Aruna ; Davey, N. ; Frank, Ray. / Recognizing facial expressions : Computational models and humans. 2013 13th UK Workshop on Computational Intelligence, UKCI 2013. IEEE, 2013. pp. 191-198

Bibtex

@inproceedings{6d4c770820ea4259b1d1d8b0d97b3669,
title = "Recognizing facial expressions: Computational models and humans",
abstract = "This paper discusses various biologically plausible computational models that recognize human facial expression and analyze them. Identifying facial expressions is a non trivial task for a human and is a key part of social interactions. However, it is not as simple as that for a computational system. Here we analyze six different universally accepted facial expressions for analysis with the aid of six biologically plausible computational models. There have been a limited number of studies comparing the performance of human subjects with computational models for facial expression recognition. This paper does a genuine attempt in making this comparison.",
keywords = "Curvilinear Component Analysis, Facial expression, Gabor filters, human subjects, Principal component analysis, Support Vector machines",
author = "Aruna Shenoy and N. Davey and Ray Frank",
year = "2013",
month = dec,
day = "31",
doi = "10.1109/UKCI.2013.6651305",
language = "English",
isbn = "9781479915682",
pages = "191--198",
booktitle = "2013 13th UK Workshop on Computational Intelligence, UKCI 2013",
publisher = "IEEE",
note = "2013 13th UK Workshop on Computational Intelligence, UKCI 2013 ; Conference date: 09-09-2013 Through 11-09-2013",

}

RIS

TY - GEN

T1 - Recognizing facial expressions

T2 - 2013 13th UK Workshop on Computational Intelligence, UKCI 2013

AU - Shenoy, Aruna

AU - Davey, N.

AU - Frank, Ray

PY - 2013/12/31

Y1 - 2013/12/31

N2 - This paper discusses various biologically plausible computational models that recognize human facial expression and analyze them. Identifying facial expressions is a non trivial task for a human and is a key part of social interactions. However, it is not as simple as that for a computational system. Here we analyze six different universally accepted facial expressions for analysis with the aid of six biologically plausible computational models. There have been a limited number of studies comparing the performance of human subjects with computational models for facial expression recognition. This paper does a genuine attempt in making this comparison.

AB - This paper discusses various biologically plausible computational models that recognize human facial expression and analyze them. Identifying facial expressions is a non trivial task for a human and is a key part of social interactions. However, it is not as simple as that for a computational system. Here we analyze six different universally accepted facial expressions for analysis with the aid of six biologically plausible computational models. There have been a limited number of studies comparing the performance of human subjects with computational models for facial expression recognition. This paper does a genuine attempt in making this comparison.

KW - Curvilinear Component Analysis

KW - Facial expression

KW - Gabor filters

KW - human subjects

KW - Principal component analysis

KW - Support Vector machines

U2 - 10.1109/UKCI.2013.6651305

DO - 10.1109/UKCI.2013.6651305

M3 - Conference contribution

AN - SCOPUS:84891106173

SN - 9781479915682

SP - 191

EP - 198

BT - 2013 13th UK Workshop on Computational Intelligence, UKCI 2013

PB - IEEE

Y2 - 9 September 2013 through 11 September 2013

ER -