Skeleton-based Hand Gesture Recognition Research Articles

MVHANet: multi-view hierarchical aggregation network for skeleton-based hand gesture recognition

Skeleton-based gesture recognition (SHGR) is a very challenging task due to the complex articulated topology of hands. Previous works often learn hand characteristics from a single observation viewpoint. However, the various context information hidden in multiple viewpoints is disregarded. To resolve this issue, we propose a novel multi-view hierarchical aggregation network for SHGR. Firstly, two-dimensional non-uniform spatial sampling, a novel strategy forming extrinsic parameter distributions of virtual cameras, is presented to enumerate viewpoints to observe hand skeletons. Afterward, we adopt coordinate transformation to generate multi-view hand skeletons and employ a multi-branch convolutional neural networks to further extract the multi-view features. Furthermore, we exploit a novel hierarchical aggregation network including hierarchical attention architecture and global context modeling to fuse the multi-view features for final classification. Experiments on three benchmarked datasets demonstrate that our work can be competitive with the state-of-the-art methods.

Dynamic Hand Gesture Recognition Using Multi-Branch Attention Based Graph and General Deep Learning Model

The dynamic hand skeleton data have become increasingly attractive to widely studied for the recognition of hand gestures that contain 3D coordinates of hand joints. Many researchers have been working to develop skeleton-based hand gesture recognition systems using various discriminative spatial-temporal attention features by calculating the dependencies between the joints. However, these methods may face difficulties in achieving high performance and generalizability due to their inefficient features. To overcome these challenges, we proposed a Multi-branch attention-based graph and a general deep-learning model to recognize hand gestures by extracting all possible types of skeleton-based features. We used two graph-based neural network channels in our multi-branch architectures and one general neural network channel. In graph-based neural network channels, one channel first uses the spatial attention module and then the temporal attention module to produce the spatial-temporal features. In contrast, we produced temporal-spatial features in the second channel using the reverse sequence of the first branch. The last branch extracts general deep learning-based features using a general deep neural network module. The final feature vector was constructed by concatenating the spatial-temporal, temporal-spatial, and general features and feeding them into the fully connected layer. We included position embedding and mask operation for both spatial and temporal attention modules to track the node’s sequence and reduce the system’s computational complexity. Our model achieved 94.12%, 92.00%, and 97.01% accuracy after evaluation with MSRA, DHG, and SHREC’17 benchmark datasets, respectively. The high-performance accuracy and low computational cost proved that the proposed method outperformed the existing state-of-the-art methods.

An interactive game for rehabilitation based on real-time hand gesture recognition.

Currently, cardiovascular and cerebrovascular diseases have become serious global health problems related to their high incidence and fatality rate. Some patients with cardiovascular cerebro-cardiovascular diseases even may face motor or cognitive dysfunction after surgery. In recent years, human-computer interactive systems with artificial intelligence have become an important part of human well-being because they enable novel forms of rehabilitation therapies. We propose an interactive game utilizing real-time skeleton-based hand gesture recognition, which aims to assist rehabilitation exercises by improving the hand-eye coordination of the patients during a game-like experience. For this purpose, we propose a lightweight residual graph convolutional architecture for hand gesture recognition. Furthermore, we designed the whole system using the proposed gesture recognition module and some third-party modules. Finally, some participants were invited to test our system and most of them showed an improvement in their passing rate of the game during the test process.

SHREC 2021: Skeleton-based hand gesture recognition in the wild

Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more. Recognition of hand gestures can be nowadays performed directly from the stream of hand skeletons estimated by software provided by low-cost trackers (Ultraleap) and MR headsets (Hololens, Oculus Quest) or by video processing software modules (e.g. Google Mediapipe). Despite the recent advancements in gesture and action recognition from skeletons, it is unclear how well the current state-of-the-art techniques can perform in a real-world scenario for the recognition of a wide set of heterogeneous gestures, as many benchmarks do not test online recognition and use limited dictionaries. This motivated the proposal of the SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild. For this contest, we created a novel dataset with heterogeneous gestures featuring different types and duration. These gestures have to be found inside sequences in an online recognition scenario. This paper presents the result of the contest, showing the performances of the techniques proposed by four research groups on the challenging task compared with a simple baseline method.

Normalized edge convolutional networks for skeleton-based hand gesture recognition

Dynamic hand skeletons consisting of discrete spatial-temporal finger joint clouds effectively convey the intentions of communicators. Previous graph convolutional networks (GCNs) relying on human hand-crafted inductive biases have been quickly promoted for skeleton-based hand gesture recognition (SHGR). However, most existing graph constructions for GCN-based solutions are set manually, only considering the physical topology of the hand skeleton, and the fixed dependencies among hand joints may lead to suboptimal models. To enrich the local dependencies, we emphasize that hand skeletons can be seen from two views: explicit joint clouds and implicit skeleton topology. Starting from those two views of hand gestures, we attempt to introduce dynamics and diversities into the local neighborhood of the graph by dividing it into sets of physical neighbors, temporal neighbors and varying neighbors. Next, we systematically proceed with three innovations, including the novel edge-varying graph, normalized edge convolution operation, and zig-zag sampling strategy, to alleviate the challenges resulting from engineering practices. Finally, spatial-based GCNs called normalized edge convolutional networks are constructed for hand gesture recognition. Experiments on publicly available hand datasets show that our work is stable for performing state-of-the-art gesture recognition, and ablation experiments are also provided to validate each contribution.

STA-GCN: two-stream graph convolutional network with spatial–temporal attention for hand gesture recognition

Skeleton-based hand gesture recognition is an active research topic in computer graphics and computer vision and has a wide range of applications in VR/AR and robotics. Although the spatial–temporal graph convolutional network has been successfully used in skeleton-based hand gesture recognition, these works often use a fixed spatial graph according to the hand skeleton tree or use a fixed graph on the temporal dimension, which may not be optimal for hand gesture recognition. In this paper, we propose a two-stream graph attention convolutional network with spatial–temporal attention for hand gesture recognition. We adopt pose stream and motion stream as the two input streams for our network. In pose stream, we use the joint in each frame as the input; In motion stream, we use the joint offsets between neighboring frames as the input. We propose a new temporal graph attention module to model the temporal dependency and also use a spatial graph attention module to construct dynamic skeleton graph. For each stream, we adopt graph convolutional network with spatial–temporal attention to extract the features. Then, we concatenate the feature of the pose stream and motion stream for gesture recognition. We achieve the competitive performance on the main hand gesture recognition benchmark datasets, which demonstrates the effectiveness of our method.