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Geometry vs appearance for discriminating between posed and spontaneous emotions

Zhang, Ligang, Tjondronegoro, Dian W., & Chandran, Vinod (2011) Geometry vs appearance for discriminating between posed and spontaneous emotions. Neural Information Processing : Lecture Notes in Computer Science, 7064/2011, pp. 431-440.

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Abstract

Spontaneous facial expressions differ from posed ones in appearance, timing and accompanying head movements. Still images cannot provide timing or head movement information directly. However, indirectly the distances between key points on a face extracted from a still image using active shape models can capture some movement and pose changes. This information is superposed on information about non-rigid facial movement that is also part of the expression. Does geometric information improve the discrimination between spontaneous and posed facial expressions arising from discrete emotions? We investigate the performance of a machine vision system for discrimination between posed and spontaneous versions of six basic emotions that uses SIFT appearance based features and FAP geometric features. Experimental results on the NVIE database demonstrate that fusion of geometric information leads only to marginal improvement over appearance features. Using fusion features, surprise is the easiest emotion (83.4% accuracy) to be distinguished, while disgust is the most difficult (76.1%). Our results find different important facial regions between discriminating posed versus spontaneous version of one emotion and classifying the same emotion versus other emotions. The distribution of the selected SIFT features shows that mouth is more important for sadness, while nose is more important for surprise, however, both the nose and mouth are important for disgust, fear, and happiness. Eyebrows, eyes, nose and mouth are important for anger.

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ID Code: 44130
Item Type: Journal Article
Keywords: Facial expression, posed, spontaneous, SIFT, FAP
DOI: 10.1007/978-3-642-24965-5_49
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2011 Springer-Verlag GmbH Berlin Heidelberg
Copyright Statement: The definitive version is available from http://www.springerlink.com/
Deposited On: 23 Aug 2011 08:03
Last Modified: 24 Jul 2014 15:59

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