Unilateral Facial Action Unit Detection: Revealing Nuanced Facial Expressions

Facial expressions are essential for non-verbal human communication as they convey behavioral intentions and emotional states. While facial action units (AUs) can occur bilaterally or unilaterally, the existing research in affective computing predominantly concentrates on bilateral expressions, largely due to the lack of datasets with unilateral AU labels. In this study, we present a method for generating unilateral AU labels and assess its efficacy against expert-labeled facial images. Furthermore, we introduce a dedicated model trained on the generated data and evaluate its performance across multiple datasets. Our findings offer insights into feature extraction for unilateral facial expression recognition. This research contributes to advancing the understanding and recognition of nuanced facial expressions, with potential applications in various domains such as healthcare and human-computer interaction.