A Novel Dynamic Model Capturing Spatial and Temporal Patterns for Facial Expression Analysis

Facial expression analysis could be greatly improved by incorporating spatial and temporal patterns present in facial behavior, but the patterns have not yet been utilized to their full advantage. We remedy this via a novel dynamic model-an interval temporal restricted Boltzmann machine (IT-RBM) - that is able to capture both universal spatial patterns and complicated temporal patterns in facial behavior for facial expression analysis. We regard a facial expression as a multifarious activity composed of sequential or overlapping primitive facial events. Allen's interval algebra is implemented to portray these complicated temporal patterns via a two-layer Bayesian network. The nodes in the upper-most layer are representative of the primitive facial events, and the nodes in the lower layer depict the temporal relationships between those events. Our model also captures inherent universal spatial patterns via a multi-value restricted Boltzmann machine in which the visible nodes are facial events, and the connections between hidden and visible nodes model intrinsic spatial patterns. Efficient learning and inference algorithms are proposed. Experiments on posed and spontaneous expression distinction and expression recognition demonstrate that our proposed IT-RBM achieves superior performance compared to state-of-the art research due to its ability to incorporate these facial behavior patterns.