Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study
This work provides a practical evaluation for researchers in computer vision and facial expression analysis, but it is incremental as it assesses existing methods rather than proposing new ones.
The study evaluated various optical flow techniques for facial expression recognition across multiple datasets, finding that motion approximation methods significantly impact performance when encoding facial motion.
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. The aim of this work is not to propose a new expression recognition technique, but to understand better the adequacy of existing state-of-the art optical flow for encoding facial motion in the context of facial expression recognition. Our evaluations highlight the fact that motion approximation methods used to overcome motion discontinuities have a significant impact when optical flows are used to characterize facial expressions.