One-shot Learning Gesture Recognition Based on Evolution of Discrimination with Successive Memory

In this paper, a one-shot learning gesture recognition algorithm based on evolution of discrimination with successive memory is presented, which utilizes the transferability of large-scale pre-trained DNN (Deep Neural Network) gesture recognition model and distance discrimination to carry out high-performance recognition with evolutionary discrimination. Our scheme can be narrated as follows. Firstly, a DNN gesture recognition model is proactively trained by a sample set with 19 classes of BSG dataset as a transferable model with its powerful extractor of features. Secondly, the transferable capacity of extractor is employed to extract features of labeled root samples and test samples respectively towards one-shot learning gesture recognition so as to achieve a high performance feature extraction and structured arraying. Finally, the discriminative recognition can be carried out with Euclidean distance measure between the root features and test features. Meanwhile a mechanism of updating and evolution of root features memory is built and utilized for one-shot learning gesture recognition so as to enhance the performance of recognition. A kind of software for online one-shot learning gesture recognition towards practical applications is designed and developed to achieve outstanding performance with fast response speed and high recognition accuracy. A series of experiments on the additional 10 classes of BSG dataset are conducted to verify and validate the performance advantages of our proposed one-shot learning gesture recognition algorithm.