Biologically plausible computational models for facial expression recognition

Aruna Shenoy; N. Davey; Raymond Frank

doi:10.1109/CEEC.2013.6659442

Biologically plausible computational models for facial expression recognition

Aruna Shenoy, N. Davey, Raymond 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.

Original language	English
Title of host publication	2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 - Conference Proceedings
Pages	39-44
Number of pages	6
DOIs	https://doi.org/10.1109/CEEC.2013.6659442
Publication status	Published - 1 Dec 2013
Event	2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 - Colchester, United Kingdom Duration: 17 Sept 2013 → 18 Sept 2013

Conference

Conference	2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013
Country/Territory	United Kingdom
City	Colchester
Period	17/09/13 → 18/09/13

Keywords

Curvilinear Component Analysis
facial expressions
Gabor filters
Principal Component Analysis
Support Vector Machines

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10.1109/CEEC.2013.6659442

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@inproceedings{0329234c9f28445783b5be52c278ba32,

title = "Biologically plausible computational models for facial expression recognition",

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.",

keywords = "Curvilinear Component Analysis, facial expressions, Gabor filters, Principal Component Analysis, Support Vector Machines",

author = "Aruna Shenoy and N. Davey and Raymond Frank",

year = "2013",

month = dec,

day = "1",

doi = "10.1109/CEEC.2013.6659442",

language = "English",

isbn = "9781479903818",

pages = "39--44",

booktitle = "2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 - Conference Proceedings",

note = "2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 ; Conference date: 17-09-2013 Through 18-09-2013",

}

Shenoy, A, Davey, N & Frank, R 2013, Biologically plausible computational models for facial expression recognition. in 2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 - Conference Proceedings., 6659442, pp. 39-44, 2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013, Colchester, United Kingdom, 17/09/13. https://doi.org/10.1109/CEEC.2013.6659442

TY - GEN

T1 - Biologically plausible computational models for facial expression recognition

AU - Shenoy, Aruna

AU - Davey, N.

AU - Frank, Raymond

PY - 2013/12/1

Y1 - 2013/12/1

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.

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.

KW - Curvilinear Component Analysis

KW - facial expressions

KW - Gabor filters

KW - Principal Component Analysis

KW - Support Vector Machines

U2 - 10.1109/CEEC.2013.6659442

DO - 10.1109/CEEC.2013.6659442

M3 - Conference contribution

AN - SCOPUS:84892608445

SN - 9781479903818

SP - 39

EP - 44

BT - 2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013 - Conference Proceedings

T2 - 2013 5th Computer Science and Electronic Engineering Conference, CEEC 2013

Y2 - 17 September 2013 through 18 September 2013

ER -

Biologically plausible computational models for facial expression recognition

Abstract

Conference

Keywords

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