Representation of facial expression categories in continuous arousal-valence space: Feature and correlation
Data(s) |
2014
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Resumo |
Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions, and thus has gained increasing attention over recent years. Many sub-problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems, and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal-valence dimensional space (AV). The evaluation compares the performance of texture features (SIFT, Gabor, LBP) against geometric features (FAP-based distances), and the fusion of the two. It also compares the prediction of arousal and valence, obtained using the best fusion method, to the corresponding ground truths. Spatial distribution, shift, similarity, and correlation are considered for the six basic categorized emotions (i.e. anger, disgust, fear, happiness, sadness, surprise). Using the NVIE database, results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories. |
Formato |
application/pdf |
Identificador | |
Publicador |
Elsevier Science BV |
Relação |
http://eprints.qut.edu.au/77785/1/IVC2014_1.pdf DOI:10.1016/j.imavis.2014.09.005 Zhang, Ligang, Tjondronegoro, Dian W., & Chandran, Vinod (2014) Representation of facial expression categories in continuous arousal-valence space: Feature and correlation. Image and Vision Computing, 32(12), pp. 1067-1079. |
Direitos |
Copyright 2014 Elsevier Licensed under the Creative Commons Attribution; Non-Commercial; No-Derivatives 4.0 International: 10.1016/j.imavis.2014.09.005 |
Fonte |
School of Electrical Engineering & Computer Science; School of Information Systems; Science & Engineering Faculty |
Palavras-Chave | #080104 Computer Vision #080602 Computer-Human Interaction #Facial expression recognition #Dimensional space #Continuous axis #Correlation #Categorized emotion |
Tipo |
Journal Article |