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Automatic facial expression recognition using boosted discriminatory classifiers

Moore, S and Bowden, R (2007) Automatic facial expression recognition using boosted discriminatory classifiers In: Third International Workshop on AMFG'07, 2007-10-20 - 2007-10-20, Rio de Janeiro, Brazil.

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Over the last two decades automatic facial expression recognition has become an active research area. Facial expressions are an important channel of non-verbal communication, and can provide cues to emotions and intentions. This paper introduces a novel method for facial expression recognition, by assembling contour fragments as discriminatory classifiers and boosting them to form a strong accurate classifier. Detection is fast as features are evaluated using an efficient lookup to a chamfer image, which weights the response of the feature. An Ensemble classification technique is presented using a voting scheme based on classifiers responses. The results of this research are a 6-class classifier (6 basic expressions of anger, joy, sadness, surprise, disgust and fear) which demonstrate competitive results achieving rates as high as 96% for some expressions. As classifiers are extremely fast to compute the approach operates at well above frame rate. We also demonstrate how a dedicated classifier can be consrtucted to give optimal automatic parameter selection of the detector, allowing real time operation on unconstrained video.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors : Moore, S and Bowden, R
Date : 2007
DOI : 10.1007/978-3-540-75690-3_6
Contributors :
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 23 Jan 2020 12:46

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