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Visualization of Time‐Series Data in Parameter Space for Understanding Facial Dynamics
Author(s) -
Tam G. K. L.,
Fang H.,
Aubrey A. J.,
Grant P. W.,
Rosin P. L.,
Marshall D.,
Chen M.
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01939.x
Subject(s) - computer science , visualization , visual analytics , representation (politics) , series (stratigraphy) , time series , data visualization , graph , feature vector , feature (linguistics) , space (punctuation) , data mining , artificial intelligence , data space , facial expression , pattern recognition (psychology) , machine learning , theoretical computer science , paleontology , linguistics , philosophy , politics , biology , political science , law , operating system
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.