Facial Expression Recognition with Relevance Vector Machines

Author

Datcu, D. ; Rothkrantz, L.J.M.

Author_Institution

Man-Machine Interaction Group, Delft Univ. of Technol.

fYear

2005

fDate

6-6 July 2005

Firstpage

193

Lastpage

196

Abstract

For many decades automatic facial expression recognition has scientifically been considered a real challenging problem in the fields of pattern recognition or robotic vision. The current research aims at proposing relevance vector machines (RVM) as a novel classification technique for the recognition of facial expressions in static images. The aspects related to the use of Support Vector Machines are also presented. The data for testing were selected from the Cohn-Kanade facial expression database. We report 90.84% recognition rates for RVM for six universal expressions based on a range of experiments. Some discussions on the comparison of different classification methods are included

Keywords

emotion recognition; face recognition; image classification; relevance feedback; support vector machines; visual databases; Cohn-Kanade facial expression database; RVM; automatic facial expression recognition; classification technique; relevance vector machine; static image; support vector machine; Face detection; Face recognition; Facial features; Humans; Man machine systems; Pattern recognition; Robot vision systems; Robotics and automation; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on

Conference_Location

Amsterdam

Print_ISBN

0-7803-9331-7

Type

conf

DOI

10.1109/ICME.2005.1521393

Filename

1521393