Feature Ranking Ensembles for Facial Action Unit Classification

Authors:
Terry Windeatt;Kaushala Dias
Affiliations:
Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, Guildford, United Kingdom GU2 7XH;Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, Guildford, United Kingdom GU2 7XH
Venue:
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Year:
2008

Citing 16
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Abstract

Recursive Feature Elimination RFE combined with feature-ranking is an effective technique for eliminating irrelevant features. In this paper, an ensemble of MLP base classifiers with feature-ranking based on the magnitude of MLP weights is proposed. This approach is compared experimentally with other popular feature-ranking methods, and with a Support Vector Classifier SVC. Experimental results on natural benchmark data and on a problem in facial action unit classification demonstrate that the MLP ensemble is relatively insensitive to the feature-ranking method, and simple ranking methods perform as well as more sophisticated schemes. The results are interpreted with the assistance of bias/variance of 0/1 loss function.