# Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study

arXiv: https://arxiv.org/abs/1904.11592 · HTML: https://mlsanity.com/paper/1904.11592/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Spiking-Fer: Spiking Neural Network for Facial Expression Recognition With Event Cameras](https://mlsanity.com/paper/2304.10211/) (2023)
- [Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition](https://mlsanity.com/paper/2204.07636/) (2022)
- [Facial Expression Recognition with Controlled Privacy Preservation and Feature Compensation](https://mlsanity.com/paper/2412.00277/) (2024)
- [eMotion-GAN: A Motion-based GAN for Photorealistic and Facial Expression Preserving Frontal View Synthesis](https://mlsanity.com/paper/2404.09940/) (2024)
- [SnatchML: Hijacking ML models without Training Access](https://mlsanity.com/paper/2406.01708/) (2024)
- [SRFlow: A Dataset and Regularization Model for High-Resolution Facial Optical Flow via Splatting Rasterization](https://mlsanity.com/paper/2601.06479/) (2026)

## Abstract (arXiv)

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.