Holoscopic 3D Microgesture Recognition by Deep Neural Network Model Based on Viewpoint Images and Decision Fusion

Finger microgestures have been widely used in human computer interaction (HCI), particularly for interactive applications, such as virtual reality (VR) and augmented reality (AR) technologies, to provide immersive experience. However, traditional 2D image-based microgesture recognition suffers from low accuracy due to the limitations of 2D imaging sensors, which have no depth information. In this article, we proposed an innovative 3D microgesture recognition system based on a holoscopic 3D imaging sensor. Due to the lack of holoscopic 3D datasets, a comprehensive holoscopic 3D microgesture (HoMG) database is created and used to develop a robust 3D microgesture recognition method. Then, a fast algorithm is proposed to extract multiviewpoint images from one holoscopic image. Furthermore, we applied a CNN model with an attention-based residual block to each viewpoint image to improve the algorithm performance. Finally, bagging classification tree decision-level fusion is applied to combine the predictions. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods and delivers a better accuracy than existing methods.