Infant and toddler facial expression recognition based on topology-aware pooling graph U-Net
TL;DR
This paper proposes a Topology-Aware Pooling Graph U-Net for infant facial expression recognition in natural scenes, the first work to introduce Graph U-Net into this domain and demonstrates its robustness and effectiveness.
Abstract
Facial expressions are essential for infants and toddlers to communicate emotions and interact socially. Accurate recognition of these expressions is vital for understanding their needs and enhancing adult-child communication. However, this task is challenging due to the subtlety of infant expressions, environmental interference, and the inherent small inter-class but large intra-class differences in early emotional development. To address these challenges, this paper proposes a Topology-Aware Pooling Graph U-Net for infant facial expression recognition in natural scenes. This is the first work to introduce Graph U-Net into this domain. The model constructs graph structures to capture similarity relationships among feature vectors, enabling deep exploration of topological information in high-dimensional features. A novel topology-aware graph pooling mechanism is designed to effectively assess node importance within the graph. Experimental results on the IFER dataset show that the proposed method achieves 93.22% accuracy, outperforming state-of-the-art approaches. It excels in distinguishing visually similar expressions such as "startle" (90.41%) and "crying" (95.03%), demonstrating its robustness and effectiveness.