Combining Stochastic and Deterministic Search for Pose-Invariant Facial Expression Recognition

We propose a novel method for pose-invariant facial expression recognition from monocular video sequences that combines stochastic and deterministic search processes. We use the simple face model called variable-intensity template, which can be prepared with very little time and effort. We tackle the two issues found in previous work on the variable-intensity template: low accuracy in head pose estimation, and assumption violations due to external intensity changes such as illumination change. We mitigate these issues by introducing the deterministic approach into the stochastic approach implemented as a particle filter. Our experiment demonstrates significant improvements in recognition performance for horizontal and vertical head orientations in the range of ±40 degrees and ±20 degrees, respectively, from the frontal view.