Facial expression recognition using Support Vector Machines

Author

Abdulrahman, Muzammil ; Eleyan, Alaa

Author_Institution

Electr. & Electron. Eng. Dept., Mevlana Univ. Konya, Konya, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

276

Lastpage

279

Abstract

This paper propose a facial expression recognition approach based on Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. Experiments were carried out on the Japanese Female Facial Expression (JAFFE) database and our recently introduced Mevlana University Facial Expression (MUFE) database. Support Vector Machine (SVM) was used as classifier. In all conducted experiments on JAFFE and MUFE databases, obtained results reveal that PCA+SVM has an average recognition rate of 87% and 77%, respectively.

Keywords

emotion recognition; face recognition; feature extraction; principal component analysis; support vector machines; visual databases; JAFFE database; Japanese Female Facial Expression database; LBP algorithm; MUFE database; Mevlana University Facial Expression database; PCA; SVM; average recognition rate; facial expression recognition; local binary pattern algorithm; principal component analysis; support vector machines; Conferences; Databases; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; Facial Expression Recognition; Local Binary Patterns; Principal Component Analysis; Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7129813

Filename

7129813