Automatic facial expression recognition with AAM-Based feature extraction and SVM classifier

Authors:
Xiaoyi Feng;Baohua Lv;Zhen Li;Jiling Zhang
Affiliations:
School of Electronic and Information, Northwestern Polytechnic University, Xi'an, China;School of Electronic and Information, Northwestern Polytechnic University, Xi'an, China;School of Electronic and Information, Northwestern Polytechnic University, Xi'an, China;School of Electronic and Information, Northwestern Polytechnic University, Xi'an, China
Venue:
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Year:
2006

Citing 4
Cited 0

Automatic Classification of Single Facial Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition using fisher weight maps

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Facial expression recognition using kernel canonical correlation analysis (KCCA)

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an effective method is proposed for automatic facial expression recognition from static images. First, a modified Active Appearance Model (AAM) is used to locate facial feature points automatically. Then, based on this, facial feature vector is formed. Finally, SVM classifier with a sample selection method is adopted for expression classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 69.9% for novel expressers, showing that the proposed method is promising.