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

Raymond S. Smith, Terry Windeatt
Proceedings of the First Workshop on Applications of Pattern Analysis, PMLR 11:111-118, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v11-smith10a, title = {Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling}, author = {Smith, Raymond S. and Windeatt, Terry}, booktitle = {Proceedings of the First Workshop on Applications of Pattern Analysis}, pages = {111--118}, year = {2010}, editor = {Diethe, Tom and Cristianini, Nello and Shawe-Taylor, John}, volume = {11}, series = {Proceedings of Machine Learning Research}, address = {Cumberland Lodge, Windsor, UK}, month = {01--03 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v11/smith10a/smith10a.pdf}, url = {https://proceedings.mlr.press/v11/smith10a.html}, 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.} }
Endnote
%0 Conference Paper %T Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling %A Raymond S. Smith %A Terry Windeatt %B Proceedings of the First Workshop on Applications of Pattern Analysis %C Proceedings of Machine Learning Research %D 2010 %E Tom Diethe %E Nello Cristianini %E John Shawe-Taylor %F pmlr-v11-smith10a %I PMLR %P 111--118 %U https://proceedings.mlr.press/v11/smith10a.html %V 11 %X 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.
RIS
TY - CPAPER TI - Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling AU - Raymond S. Smith AU - Terry Windeatt BT - Proceedings of the First Workshop on Applications of Pattern Analysis DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-smith10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 11 SP - 111 EP - 118 L1 - http://proceedings.mlr.press/v11/smith10a/smith10a.pdf UR - https://proceedings.mlr.press/v11/smith10a.html AB - 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. ER -
APA
Smith, R.S. & Windeatt, T.. (2010). Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling. Proceedings of the First Workshop on Applications of Pattern Analysis, in Proceedings of Machine Learning Research 11:111-118 Available from https://proceedings.mlr.press/v11/smith10a.html.

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