Towards children's empathy ability analysis: joint facial expression recognition and intensity estimation using label distribution learning

Empathy ability is one of the most important social communication skills in early childhood development. To analyze the children's empathy ability, facial expression analysis (FEA) is an effective way due to its ability to understand children' emotional states. Previous works mainly focus on recognizing the facial expression categories yet fail to estimate expression intensity, the latter of which is more important for fine-grained emotion analysis. To this end, this paper firstly proposes to analyze children's empathy ability with both the categories and the intensities of facial expressions. A novel FEA method based on intensity label distribution learning is presented, which aims to recognize expression categories and estimate their intensity levels in an end-to-end framework. Numerous experiments validate that the proposed method is promising in analysis of the differences in empathy ability between typically developing children and children with autism spectrum disorder