Multi-model ensemble gesture recognition network for high-accuracy dynamic hand gesture recognition

Hand gesture and action recognition have been extensively researched in the past two decades due to the emerging advanced acquisition and interaction technologies, which open the floodgates for a vast range of potential applications. Particularly, many spatial–temporal feature extractors have been proposed, such as RNNs-based models, temporal convolutional network (TCN), and 3D convolutional neural networks (3DCNN) for modeling long-term dependencies in sequential data. However, it remains challenging to obtain a high recognition rate because of the difficulty of effectively extracting spatial–temporal features and efficiently classifying them with noisy and complex skeleton sequences. Therefore, this paper proposes a deep ensemble framework called multi-model ensemble gesture recognition network (MMEGRN) for skeleton-based hand gesture recognition. Specifically, to establish effective feature extraction and accurate gesture recognition, we propose an architecture consisting of four sub-networks, three spatio-temporal features classifiers to leverage their various capabilities of extracting and classifying skeleton sequences. Through late feature fusion, the features resulted from the feature extractors of each sub-network are fused into a new fusion classifier. Each subnetwork is trained independently to perform the task of gesture recognition using only skeleton joints. The training is performed using the cyclic annealing learning rate to generate a series of models that are combined in an ensemble using the optimized weighted ensemble (OWE) method. The proposed framework combines deep learning and ensemble strengths to establish a new deep-learning network architecture for more accurate and efficient hand gesture recognition. Extensive experiments on three skeleton-based hand gesture recognition datasets have shown the effectiveness of the proposed framework and the superiority over other models in terms of recognition accuracy.