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  • Representation of facial expression categories in continuous arousal-valence space: Feature and correlation

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    Accepted Manuscript (AM)
    Author(s)
    Zhang, Ligang
    Tjondronegoro, Dian
    Chandran, Vinod
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Chandran, Vinod
    Year published
    2014
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    Abstract
    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 the 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 ...
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    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 the 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 (AV) dimensional space. 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.
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    Journal Title
    Image and Vision Computing
    Volume
    32
    Issue
    12
    DOI
    https://doi.org/10.1016/j.imavis.2014.09.005
    Copyright Statement
    © 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Science & Technology
    Physical Sciences
    Computer Science, Artificial Intelligence
    Computer Science, Software Engineering
    Publication URI
    http://hdl.handle.net/10072/390253
    Collection
    • Journal articles

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