Sign Language Recognition Based On Facial Expression and Hand Skeleton

TL;DR

This paper introduces a sign language recognition method that combines hand skeleton features and facial expressions to improve accuracy and robustness, validated on Argentinian and Chinese sign language datasets.

Contribution

It proposes a novel network integrating hand skeleton and facial expression features, with a coordinate transformation-based hand skeleton extraction method, enhancing recognition performance.

Findings

Improved recognition accuracy on Argentinian Sign Language Dataset

Enhanced robustness across different data with interference

Effective integration of facial and hand features

Abstract

Sign language is a visual language used by the deaf and dumb community to communicate. However, for most recognition methods based on monocular cameras, the recognition accuracy is low and the robustness is poor. Even if the effect is good on some data, it may perform poorly in other data with different interference due to the inability to extract effective features. To solve these problems, we propose a sign language recognition network that integrates skeleton features of hands and facial expression. Among this, we propose a hand skeleton feature extraction based on coordinate transformation to describe the shape of the hand more accurately. Moreover, by incorporating facial expression information, the accuracy and robustness of sign language recognition are finally improved, which was verified on A Dataset for Argentinian Sign Language and SEU's Chinese Sign Language Recognition Database (SEUCSLRD).