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 = {Raymond S. Smith and Terry Windeatt}, pages = {111--118}, year = {2010}, editor = {Tom Diethe and Nello Cristianini and John Shawe-Taylor}, 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 = {http://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 %J Proceedings of Machine Learning Research %P 111--118 %U http://proceedings.mlr.press %V 11 %W PMLR %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 PY - 2010/09/30 DA - 2010/09/30 ED - Tom Diethe ED - Nello Cristianini ED - John Shawe-Taylor ID - pmlr-v11-smith10a PB - PMLR SP - 111 DP - PMLR EP - 118 L1 - http://proceedings.mlr.press/v11/smith10a/smith10a.pdf UR - http://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 PMLR 11:111-118

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