Deep Fisher discriminant learning for mobile hand gesture recognition. (May 2018) - Li, Ce; Xie, Chunyu; Zhang, Baochang; Chen, Chen; Han, Jungong
Highlights: We collect a large mobile gesture database using an Andriod Huawei device, which is the largest database in published studies for mobile gesture recongnition systems. We incorporate Fisher criterion into BiLSTM network and propose F-BiLSTM and F-BiGRU to improve the traditional softmax loss training function. Extensive experiments on our MGD, BUAA Mobile Gesture database, and a public database are conducted to verify the superior performance of the proposed networks. Abstract: Gesture recognition becomes a popular analytics tool for extracting the characteristics of user movement and enables numerous practical applications in the biometrics field. Despite recent advances in this technique, complex user interaction and the limited amount of data pose serious challenges to existing methods. In this paper, we present a novel approach for hand gesture recognition based on user interaction on mobile devices. We have developed two deep models by integrating Bidirectional Long-Short Term Memory (BiLSTM) network and Bidirectional Gated Recurrent Unit (BiGRU) with Fisher criterion, termed as F-BiLSTM and F-BiGRU respectively. These two Fisher discriminative models can classify user's gesture effectively by analyzing the corresponding acceleration and angular velocity data of hand motion. In addition, we build a large Mobile Gesture Database (MGD) containing 5547 sequences of 12 gestures. With extensive experiments, we demonstrate the superior performance of the proposed method compared to the state-of-the-art BiLSTM and BiGRU on MGD database and two other benchmark databases ( i.e., BUAA mobile gesture and SmartWatch gesture). The source code and MGD database will be made publicly available at https://github.com/bczhangbczhang/Fisher-Discriminant-LSTM.