Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling.
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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 11 . 111 - 118. ISSN 1532-4435
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Official URL: http://jmlr.csail.mit.edu/proceedings/papers/v11/s...
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 |
| ID Code: | 7128 |
| Deposited By: | Symplectic Elements |
| Deposited On: | 16 Sep 2011 17:21 |
| Last Modified: | 24 Jan 2013 09:12 |
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