Recognizing facial expressions: Computational models and humans

Aruna Shenoy; N. Davey; Ray Frank

doi:10.1109/UKCI.2013.6651305

Recognizing facial expressions: Computational models and humans

Aruna Shenoy
, N. Davey
, Ray Frank

Centre for AI and Robotics Research

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

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.

Original language	English
Title of host publication	2013 13th UK Workshop on Computational Intelligence, UKCI 2013
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	191-198
Number of pages	8
ISBN (Print)	9781479915682
DOIs	https://doi.org/10.1109/UKCI.2013.6651305
Publication status	Published - 31 Dec 2013
Event	2013 13th UK Workshop on Computational Intelligence, UKCI 2013 - Guildford, Surrey, United Kingdom Duration: 9 Sept 2013 → 11 Sept 2013

Conference

Conference	2013 13th UK Workshop on Computational Intelligence, UKCI 2013
Country/Territory	United Kingdom
City	Guildford, Surrey
Period	9/09/13 → 11/09/13

Keywords

Curvilinear Component Analysis
Facial expression
Gabor filters
human subjects
Principal component analysis
Support Vector machines

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10.1109/UKCI.2013.6651305

Cite this

@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 = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2013 13th UK Workshop on Computational Intelligence, UKCI 2013 ; Conference date: 09-09-2013 Through 11-09-2013",

}

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, Institute of Electrical and Electronics Engineers (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

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 - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 9 September 2013 through 11 September 2013

ER -

Recognizing facial expressions: Computational models and humans

Abstract

Conference

Keywords

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