DocumentCode :
3402481
Title :
A novel method of facial expression recognition based on GPLVM Plus SVM
Author :
Huang, M.W. ; Wang, Z.W. ; Ying, Z.L.
Author_Institution :
Sch. of Inf. Eng., WUYI Univ., Jiangmen, China
fYear :
2010
fDate :
24-28 Oct. 2010
Firstpage :
916
Lastpage :
919
Abstract :
The dimensionality reduction has always been a long lasting thorny problem on the study of facial expression recognition (FER). In this paper, we propose a novel method of facial expression recognition, which using Gaussian process latent variable models (GPLVM) for reducing the high dimensional data of facial expression images into a relatively low dimension data and using support vector machine (SVM) classifier for the expression classification lately. By applying this algorithm to Japanese Female Facial Expression (JAFFE) database for facial expression recognition, we find that the proposed new algorithm has a better performance than the traditional algorithms, such as PCA and LDA etc. This have further proved the effectiveness of our proposed algorithm.
Keywords :
Gaussian processes; face recognition; image classification; support vector machines; GPLVM; Gaussian process latent variable models; SVM; facial expression recognition; support vector machine classifier; Classification algorithms; Databases; Face recognition; Gaussian processes; Kernel; Principal component analysis; Support vector machines; Facial expression recognition(FER); GPLVM; LDA; PCA; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing (ICSP), 2010 IEEE 10th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-5897-4
Type :
conf
DOI :
10.1109/ICOSP.2010.5655729
Filename :
5655729
Link To Document :
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