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  • Towards robust automatic affective classification of images using facial expressions for practical applications

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    Tjondronegoro224532-Accepted.pdf (1.060Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Zhang, Ligang
    Tjondronegoro, Dian
    Chandran, Vinod
    Eggink, Jana
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Chandran, Vinod
    Year published
    2016
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    Abstract
    Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. ...
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    Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.
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    Journal Title
    Multimedia Tools and Applications
    Volume
    75
    Issue
    8
    DOI
    https://doi.org/10.1007/s11042-015-2497-5
    Copyright Statement
    © 2016 Springer. This is an electronic version of an article published in Multimedia Tools and Applications, 75, 4669–4695 (2016). Multimedia Tools and Applications is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Artificial intelligence
    Software engineering
    Science & Technology
    Computer Science, Software Engineering
    Computer Science, Theory & Methods
    Publication URI
    http://hdl.handle.net/10072/390247
    Collection
    • Journal articles

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