Facial Action Unit Recognition using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers

Smith, RS and Windeatt, T (2011) Facial Action Unit Recognition using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers Studies in Computational Intelligence, 373/20. pp. 1-20.

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Abstract

Within the context face expression classification using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). The method adopted is to train a single error-correcting output code (ECOC) multiclass classifier to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to calibrate the ECOC outputs to probabilities and appropriate sums of these probabilities are taken to obtain a separate probability for each AU individually. Feature extraction is performed by generating a large number of local binary pattern (LBP) features and then selecting from these using fast correlation-based filtering (FCBF). The bias and variance properties of the classifier are measured and we show that both these sources of error can be reduced by enhancing ECOC through the application of bootstrapping and class-separability weighting.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Smith, RS and Windeatt, T
Date :	2011
DOI :	10.1007/978-3-642-22910-7_1
Additional Information :	The original publication is available at <a href="http://www.springerlink.com/content/5677244w70071102/"</a>
Depositing User :	Symplectic Elements
Date Deposited :	17 Feb 2012 08:06
Last Modified :	06 Jul 2019 05:09
URI:	http://epubs.surrey.ac.uk/id/eprint/37255

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