Geometric-Aware Facial Landmark Emotion Recognition

Abstract

Emotion recognition is crucial for enhancing human-computer interaction. While traditional approaches largely depend on RGB images, our proposed Geometric-Aware Facial Landmark Emotion Recognition framework harnesses the geometric and spectral attributes of facial landmarks for emotion recognition. This work unfolds three key contributions: utilizing Graph Convolutional Networks to grasp the natural spatial relationships among facial landmarks, introducing distance-aware graph operations to accentuate the relational geometry, and employing spectral encodings to comprehend the frequency-based attributes of landmark positions. Through rigorous experiments on recognized datasets RAF-DB and KDEF, Our method surpasses baseline methods, demonstrating its effectiveness especially in high-resolution scenarios where detailed facial landmarks are apparent. The datasets curated for Facial Landmark Emotion Recognition will also be shared publicly as part of this work, offering a significant resource for the community. The encouraging results highlight the potential of geometric-aware analysis in propelling emotion recognition systems forward, opening avenues for further research in this evolving domain.