The Importance of Facial Features in Vision-based Sign Language Recognition: Eyes, Mouth or Full Face?

TL;DR

This study systematically evaluates the importance of different facial regions in vision-based sign language recognition, revealing that the mouth is the most critical feature for improving recognition accuracy.

Contribution

It introduces a comprehensive analysis of facial regions using deep learning models, demonstrating the significance of the mouth in automatic sign language recognition.

Findings

The mouth significantly improves recognition accuracy.

Deep learning models effectively identify key facial features.

Facial features are essential for accurate sign language recognition.

Abstract

Non-manual facial features play a crucial role in sign language communication, yet their importance in automatic sign language recognition (ASLR) remains underexplored. While prior studies have shown that incorporating facial features can improve recognition, related work often relies on hand-crafted feature extraction and fails to go beyond the comparison of manual features versus the combination of manual and facial features. In this work, we systematically investigate the contribution of distinct facial regionseyes, mouth, and full faceusing two different deep learning models (a CNN-based model and a transformer-based model) trained on an SLR dataset of isolated signs with randomly selected classes. Through quantitative performance and qualitative saliency map evaluation, we reveal that the mouth is the most important non-manual facial feature, significantly improving accuracy. Our findings highlight the necessity of incorporating facial features in ASLR.