2022/01/19 by Lee Kezar, Pei Zhou, Kezar, Lee +1
Arts and Humanities · Computer Science · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Hearing Impairment and Communication #Linguistics, Language Diversity, and Identity
paper · pdf · doi:10.48550/arxiv.2201.07906
openalex publication_date 2022/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.