The Role of Facial Expressions and Emotion in ASL
This addresses a gap in understanding emotional communication in ASL for sign language research and technology, though it appears incremental as it builds on existing annotation and datasets.
The study tackled the problem of quantifying relationships between facial expressions and emotionality in American Sign Language, finding that a simple classifier can predict broad emotional categories from facial features alone.
There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language. In this final report, we provide two methods for studying these relationships through probability and prediction. Using a large corpus of natural signing manually annotated with facial features paired with lexical emotion datasets, we find that there exist many relationships between emotionality and the face, and that a simple classifier can predict what someone is saying in terms of broad emotional categories only by looking at the face.