Style Extractor for Facial Expression Recognition in the presence of Speech

The performance of facial expression recognition (FER) systems has improved with recent advances in machine learning. While studies have reported impressive accuracies in detecting emotion from posed expressions in static images, there are still important challenges in developing FER systems for videos, especially in the presence of speech. Speech articulation modulates the orofacial area, changing the facial appearance. These facial movements induced by speech introduce noise, reducing the performance of an FER system. Solving this problem is important if we aim to study more naturalistic environment or applications in the wild. We propose a novel approach to compensate for lexical information that does not require phonetic information during inference. The approach relies on a style extractor model, which creates emotional-to-neutral transformations. The transformed facial representations are spatially contrasted with the original faces, highlighting the emotional information conveyed in the video. The results demonstrate that adding the proposed style extractor model to a dynamic FER system improves the performance by 7% (absolute) compared to a similar model with no style extractor. This novel feature representation also improves the generalization of the model.