Temporal-based support vector machines for facial expression recognition


Autoria(s): Chew, Sien Wei
Data(s)

2010

Resumo

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/33215/

Relação

http://eprints.qut.edu.au/33215/1/c33215.pdf

http://www.autocrc.com/newsevents-events.htm

Chew, Sien Wei (2010) Temporal-based support vector machines for facial expression recognition. In AutoCRC Student Conference 2010, 27 July 2010, Melbourne.

Direitos

Copyright 2010 Sien Wei Chew

Fonte

Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems

Palavras-Chave #080104 Computer Vision #080109 Pattern Recognition and Data Mining #facial expression recognition #support vector machines
Tipo

Conference Paper