Title :
Facial Expression Recognition Based on SVM
Author_Institution :
Elementary Educ. Coll., Linyi Univ., Linyi, China
Abstract :
An in-depth study of the multi-classification SVM is given, and the expression classification method based on SVM is proposed for the defects of the traditional classification methods. It realizes fast classification with a relatively small sub-classifier combination, reducing the classification error. Experiments show that the multi-classification method based on SVM can obviously reduce the training and testing time and improve the classification performance.
Keywords :
face recognition; image classification; support vector machines; classification error; classification performance; expression classification method; facial expression recognition; multiclassification SVM; subclassifier combination; support vector machine; testing; training; Face; Face recognition; Feature extraction; Image recognition; Remote sensing; Support vector machines; Training; CKACFEID database; expression recognition; recognition rate; support vector machine;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2014 7th International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-6635-6
DOI :
10.1109/ICICTA.2014.69
