Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
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This paper explores the use of multisensory information fusiontechnique with Dynamic Bayesian networks (DBNs)for modeling and understanding the temporal behaviors offacial expressions in image sequences. Our approach tothe facial expression understanding lies in a probabilisticframework by integrating the DBNs with the facial actionunits (AUs) from psychological view. The DBNs provide acoherent and unified hierarchical probabilistic frameworkto represent spatial and temporal information related to facialexpressions, and to actively select the most informativevisual cues from the available information to minimize theambiguity in recognition. The recognition of facial expressionsis accomplished by fusing not only from the currentvisual observations, but also from the previous visual evidences.Consequently, the recognition becomes more robustand accurate through modeling the temporal behavior of facialexpressions. Experimental results demonstrate that ourapproach is more admissible for facial expression analysisin image sequences.