Wide Residual Network for Vision-based Static Hand Gesture Recognition

Hand gesture is a communication tool that allows messages to be conveyed, actions to be performed through hand gestures. Hence, it has the ability to simplify communication and enhance human computer interaction. This paper proposed Wide Residual Network for static hand gesture recognition. WRN improves feature propagation and gradient flows by utilizing shortcut connection in residual block. Wide residual block further improves upon residual block by increasing the width of the network and improving feature reuse, and thereby allowing the depth of the network to be trimmed and fewer trainable parameters to be learned. The network is experimented on three public datasets and compared with existing convolutional neural network (CNN) variants proposed for static hand gesture recognition.