Multimodal Gesture Recognition Using Densely Connected Convolution And Blstm

In this paper, we present a multimodal method based on densely connected convolution and bidirectional long-short-term-memory (BLSTM) for gesture recognition. The proposed method learns spatial features of gestures through the densely connected convolutional network, and then learns long-term temporal features by BLSTM network. In addition, fusion methods are evaluated on our model, and we find that fusion of features with different information can significantly improve the recognition accuracy. This purely data driven approach achieves state-of-the-art recognition accuracy on the ChaLearn LAP 2014 dataset and the Sheffield Kinect gesture (SKIG) dataset. (98.80% on the ChaLearn LAP and 99.07% on SKIG)