Share Email Print
cover

Proceedings Paper

Facial expression recognition based on improved DAGSVM
Author(s): Yuan Luo; Ye Cui; Yi Zhang
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

For the cumulative error problem because of randomization sequence of traditional DAGSVM(Directed Acyclic Graph Support Vector Machine) classification, this paper presents an improved DAGSVM expression recognition method. The method uses the distance of class and the standard deviation as the measure of the classer, which minimize the error rate of the upper structure of the classification. At the same time, this paper uses the method which combines discrete cosine transform (Discrete Cosine Transform, DCT) with Local Binary Pattern(Local Binary Pattern,LBP) ,to extract expression feature and be the input to improve the DAGSVM classifier for recognition. Experimental results show that compared with other multi-class support vector machine method, improved DAGSVM classifier can achieve higher recognition rate. And when it’s used at the platform of the intelligent wheelchair, experiments show that the method has a better robustness.

Paper Details

Date Published: 24 November 2014
PDF: 8 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 930126 (24 November 2014); doi: 10.1117/12.2072481
Show Author Affiliations
Yuan Luo, Chongqing Univ. of Posts and Telecommunications (China)
Ye Cui, Chongqing Univ. of Posts and Telecommunications (China)
Yi Zhang, Chongqing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

© SPIE. Terms of Use
Back to Top