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

RS Smith; T Windeatt

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Conference presentation

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

RS Smith and T Windeatt

Journal of Machine Learning Research Proceedings

Workshop on Applications of Pattern Analysis (Windsor, England, 31/08/2010 - 02/09/2010)

2010

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 filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.

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Title: Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling
Creators: RS Smith
T Windeatt
Publication Details: Journal of Machine Learning Research Proceedings
Conference: Workshop on Applications of Pattern Analysis (Windsor, England, 31/08/2010 - 02/09/2010)
Date published: 2010
Date submitted: 17/05/2017
Identifiers: 99512459002346
Academic Unit: University of Surrey
Resource Type: Conference presentation

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

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