# Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation

## TL;DR

This paper introduces a novel method for facial expression recognition that leverages the propagation of facial motion beyond occluded regions, improving robustness against occlusions by constructing expression-specific models.

## Contribution

It exploits facial motion propagation specificities to enhance expression recognition under occlusions, creating adaptive frameworks for better accuracy.

## Key findings

- Robust performance in presence of significant occlusions.
- Effective aggregation of expression-specific classifiers.
- Improved recognition accuracy compared to traditional methods.

## Abstract

Although much progress has been made in the facial expression analysis field, facial occlusions are still challenging. The main innovation brought by this contribution consists in exploiting the specificities of facial movement propagation for recognizing expressions in presence of important occlusions. The movement induced by an expression extends beyond the movement epicenter. Thus, the movement occurring in an occluded region propagates towards neighboring visible regions. In presence of occlusions, per expression, we compute the importance of each unoccluded facial region and we construct adapted facial frameworks that boost the performance of per expression binary classifier. The output of each expression-dependant binary classifier is then aggregated and fed into a fusion process that aims constructing, per occlusion, a unique model that recognizes all the facial expressions considered. The evaluations highlight the robustness of this approach in presence of significant facial occlusions.