An AdaBoost-based facial expression recognition method

Author

Huang, Yea-Shuan ; Chuang, Shun-hsu ; Cheng, Fang-hsuan

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Chung Hua Univ., Hsinchu, Taiwan

Volume

4

fYear

2011

fDate

10-13 July 2011

Firstpage

1648

Lastpage

1653

Abstract

A method of combining Weighted Local Directional Pattern (WLDP) and Local Binary Pattern (LBP) for facial expression recognition is proposed. First, WLDP and LBP are applied to extract human facial features. Second, principle component analysis (PCA) is used to reduce their feature dimensions respectively. Third, both reduced facial features are merged to form the final feature vector. Fourth, support vector machine (SVM) is used to recognize facial expressions. Experiment on the well known Cohn-Kanade expression database, a high accuracy rate up to 91.1% for recognizing seven expressions can be achieved with a person-independent 10-fold cross-validation scheme.

Keywords

behavioural sciences computing; face recognition; feature extraction; human computer interaction; learning (artificial intelligence); principal component analysis; support vector machines; AdaBoost based facial expression recognition method; Cohn-Kanade expression database; LBP; PCA; SVM; WLDP; feature dimensions; feature extraction; feature vector; human computer interaction; human facial features; local binary pattern; principle component analysis; support vector machine; weighted local directional pattern; Boosting; Face recognition; Image coding; Integrated circuits; Manuals; Mouth; Facial Expression Recognition; Local Binary Pattern; Principal Component Analysis; Support Vector Machine; Weighted Local Directional Pattern;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016996

Filename

6016996