Semi-Supervised RGB-D Hand Gesture Recognition via Mutual Learning of Self-Supervised Models

Human hand gesture recognition is important to human–computer interaction. Gesture recognition based on RGB and Depth (RGB-D) data exploits both RGB and depth images to provide comprehensive results. However, the research under scenario with insufficient annotated data is not adequate. In view of the problem, our insight is to perform self-supervised learning with respect to each modality, transfer the learned information to modality-specific classifiers, and then fuse their results for final decision. To this end, we propose a semi-supervised hand gesture recognition method known as Mutual Learning of Rotation-Aware Gesture Predictors (MLRAGP), which exploits unlabeled training RGB and depth images via self-supervised learning and achieves multi-modal decision fusion through deep mutual learning. For each modality, we rotate both labeled and unlabeled images to fixed angles and train an angle predictor to predict the angles, then we use the feature extraction part of the angle predictor to construct the category predictor and train it through labeled data. We subsequently fuse the category predictors about both modalities by impelling each of them to simulate the probability estimation produced by the other, and making the prediction of labeled images to approach the ground truth annotation. During the training of category predictor and mutual learning, the parameters of feature extractors can be slighted fine-tuned to avoid under-fitting. Experimental results on NTU-Microsoft Kinect Hand Gesture dataset and Washington RGB-D dataset demonstrate the superiority of this framework to existing methods.