Skeleton Aware Multi-modal Sign Language Recognition

Sign language is used by deaf or speech impaired people to communicate and requires great effort to master. Sign Language Recognition (SLR) aims to bridge between sign language users and others by recognizing words from given videos. It is an important yet challenging task since sign language is performed with fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention due to the independence on subject and background variation. Furthermore, it can be a strong complement to RGB/D modalities to boost the overall recognition rate. However, skeleton-based SLR is still under exploration due to the lack of annotations on hand keypoints. Some efforts have been made to use hand detectors with pose estimators to extract hand key points and learn to recognize sign language via a Recurrent Neural Network, but none of them outperforms RGB-based methods. To this end, we propose a novel Skeleton Aware Multi-modal SLR framework (SAM-SLR) to further improve the recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics and propose a novel Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. Our skeleton-based method achieves a higher recognition rate compared with all other single modalities. Moreover, our proposed SAM-SLR framework can further enhance the performance by assembling our skeleton-based method with other RGB and depth modalities. As a result, SAM-SLR achieves the highest performance in both RGB (98.42\%) and RGB-D (98.53\%) tracks in 2021 Looking at People Large Scale Signer Independent Isolated SLR Challenge. Our code is available at https://github.com/jackyjsy/CVPR21Chal-SLR