Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Sign language recognition(SLR) is a multidisciplinary research topic in pattern recognition and computer vision. Due to large amount of data from the continuous frames of sign language videos, selecting representative data to eliminate irrelevant information has always been a challenging problem in data preprocessing of sign language samples. In recent years, skeletal data emerged as a new type of data but received insufficient attention. Meanwhile, due to the increasing diversity of sign language features, making full use of them has also been an important research topic. In this paper, we improve keyframe-centered clips (KCC) sampling to get a new kind of sampling method called optimized keyframe-centered clips (OptimKCC) sampling to select key actions from sign language videos. Besides, we design a new kind of skeletal feature called Multi-Plane Vector Relation (MPVR) to describe the video samples. Finally, combined with the attention mechanism, we also use Attention-Based networks to distribute weights to the temporal features and the spatial features extracted from skeletal data. We implement comparison experiments on our own and the public sign language dataset under the Signer-Independent and the Signer-Dependent circumstances to show the advantages of our methods.