Sign Language Recognition Based On Facial Expression and Hand Skeleton
This work addresses communication barriers for the deaf and dumb community by enhancing sign language recognition, though it appears incremental as it combines existing modalities (hand skeleton and facial expression) with a coordinate transformation method.
The paper tackles low accuracy and poor robustness in sign language recognition from monocular cameras by proposing a network that integrates hand skeleton features with facial expression information, achieving improved performance verified on Argentinian and Chinese sign language datasets.
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).