Multiclass support vector machines and metric multidimensional scaling for facial expression recognition

Kotsia, Irene and Zafeiriou, Stefanos and Nikolaidis, Nikolaos and Pitas, Ioannis (2007) Multiclass support vector machines and metric multidimensional scaling for facial expression recognition. In: IEEE Workshop on Machine Learning for Signal Processing (MLSP 2007), 27- 29 August 2007, Thessaloniki, Greece.

Full text is not in this repository.

Official URL: http://mlsp2007.conwiz.dk/

Abstract

In this paper, a novel method for the recognition of facial expressions in videos is proposed. The system first extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. The mean Euclidean distance of the deformed grids is then calculated to create a new metric multidimensional scaling. The classification of the sample under examination to one of the 7 possible classes of facial expressions, i.e., anger, disgust, fear, happiness, sadness, surprise and neutral, is performed using multiclass SVMs defined in the new space. The experiments were performed using the Cohn-Kanade database and the results show that the above mentioned system can achieve an accuracy of 95.6%.

Item Type:Conference or Workshop Item (Paper)
Research Areas:School of Science and Technology > Science & Technology
ID Code:9615
Deposited On:06 Dec 2012 06:12
Last Modified:06 Feb 2013 11:35

Repository staff only: item control page

Full text downloads (NB count will be zero if no full text documents are attached to the record)

Downloads per month over the past year