Visualization of Time-Series Data in Parameter Space for Understanding Facial Dynamics

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dc.contributor.authorTam, Gary K. L.en_US
dc.contributor.authorFang, H.en_US
dc.contributor.authorAubrey, A. J.en_US
dc.contributor.authorGrant, P. W.en_US
dc.contributor.authorRosin, P. L.en_US
dc.contributor.authorMarshall, D.en_US
dc.contributor.authorChen, M.en_US
dc.contributor.editorH. Hauser, H. Pfister, and J. J. van Wijken_US
dc.date.accessioned2014-02-21T20:23:35Z
dc.date.available2014-02-21T20:23:35Z
dc.date.issued2011en_US
dc.description.abstractOver the past decade, computer scientists and psychologists have made great efforts to collect and analyze facial dynamics data that exhibit different expressions and emotions. Such data is commonly captured as videos and are transformed into feature-based time-series prior to any analysis. However, the analytical tasks, such as expression classification, have been hindered by the lack of understanding of the complex data space and the associated algorithm space. Conventional graph-based time-series visualization is also found inadequate to support such tasks. In this work, we adopt a visual analytics approach by visualizing the correlation between the algorithm space and our goal classifying facial dynamics. We transform multiple feature-based time-series for each expression in measurement space to a multi-dimensional representation in parameter space. This enables us to utilize parallel coordinates visualization to gain an understanding of the algorithm space, providing a fast and cost-effective means to support the design of analytical algorithms.en_US
dc.description.number3en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01939.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleVisualization of Time-Series Data in Parameter Space for Understanding Facial Dynamicsen_US
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