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

• Published in: 2010 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
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Discrete cosine transforms; Face; Face detection; Face recognition; Feature extraction; Subspace constraints; Support vector machines
• ISBN: 9781424465262; 1424465265
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2010.5580938

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.