Using SVM to design facial expression recognition for shape and texture features

This paper presents a novel facial emotion recognition (FER) technique, based on support vector machine (SVM), to recognize the facial emotion expression. Here it is called the FERS technique. First, a face detection method, which combines the Haar-like features (HFs) method with the self quotient i...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 2697 - 2704
Main Authors Hung-Hsu Tsai, Yen-Shou Lai, Yi-Cheng Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Discrete cosine transforms
Face
Face detection
Face recognition
Feature extraction
Subspace constraints
Support vector machines
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ISBN9781424465262
1424465265
ISSN2160-133X
DOI10.1109/ICMLC.2010.5580938

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Summary:This paper presents a novel facial emotion recognition (FER) technique, based on support vector machine (SVM), to recognize the facial emotion expression. Here it is called the FERS technique. First, a face detection method, which combines the Haar-like features (HFs) method with the self quotient image (SQI) filter, is used in the FERS technique to accurately locate the face region of an image. It can improve the detection rate due to the use of the SQI filter to overcome the insufficient light and shade light. Subsequently, angular radial transform (ART), discrete cosine transform (DCT) and Gabor filter (GF) are employed in the procedure of facial expression feature extraction. An SVM is trained and then utilized to recognize the facial expression for a queried face image. Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods.
ISBN:9781424465262
1424465265
ISSN:2160-133X
DOI:10.1109/ICMLC.2010.5580938