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- W81693973 abstract "This paper proposed a probabilistic approach to divide the Facial Action Units (AUs) based on the physiological relations and their strengths among the facial muscle groups. The physiological relations and their strengths were captured using a Static Bayesian Network (SBN) from given databases. A data driven spatio-temporal probabilistic scoring function was introduced to divide the AUs into : (i) frequently occurred and strongly connected AUs (FSAUs) and (ii) infrequently occurred and weakly connected AUs (IWAUs). In addition, a Dynamic Bayesian Network (DBN) based predictive mechanism was implemented to predict the IWAUs from FSAUs. The combined spatio-temporal modeling enabled a framework to predict a full set of AUs in real-time. Empirical analyses were performed to illustrate the efficacy and utility of the proposed approach. Four different datasets of varying degrees of complexity and diversity were used for performance validation and perturbation analysis. Empirical results suggest that the IWAUs can be robustly predicted from the FSAUs in real-time and was found to be robust against noise." @default.
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- W81693973 date "2011-01-01" @default.
- W81693973 modified "2023-09-23" @default.
- W81693973 title "A Spatio-Temporal Probabilistic Framework for Dividing and Predicting Facial Action Units" @default.
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- W81693973 doi "https://doi.org/10.1007/978-3-642-24571-8_74" @default.
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