Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling.

Smith, RS and Windeatt, T (2010) Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling. Journal of Machine Learning Research, Track . pp. 111-118.

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Abstract

We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based ltering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classi er.

Item Type:	Article
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Smith, RS Windeatt, T
Date :	3 September 2010
Depositing User :	Symplectic Elements
Date Deposited :	16 Sep 2011 16:21
Last Modified :	31 Oct 2017 14:08
URI:	http://epubs.surrey.ac.uk/id/eprint/7128

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