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

Authors: G. K. L. Tam1, H. Fang2, A. J. Aubrey1, P. W. Grant2, P. L. Rosin1, D. Marshall1, M. Chen2

DOI: 10.1111/j.1467-8659.2011.01939.x

Abstract:

Over 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.

Link to Paper

Authors

Dr Andrew J Aubrey

Dr Andrew J Aubrey

Analysis and synthesis of facial dynamics for animation and also further understanding of expression perception.

Prof. Paul Rosin

Prof. Paul Rosin

Various aspects of computer vision, including 2D and 3D facial analysis and synthesis.

Prof. David Marshall

Prof. David Marshall

Computer Vision including Reverse Engineering, Automated Inspection, Dynamic 2D/3D Facial Analysis.

Dr Phil Grant

Dr Phil Grant

Modelling of facial ageing and facial dynamics. Information Visualisation. Applications of genetic and logic programming.