Hand fine-motion recognition based on 3D Mesh MoSIFT feature descriptor

The times of Big Data promotes increasingly higher demands for fine-motion analysis, such as hand activity recognition. However, in real-world scenarios, hand activity recognition suffers huge challenges with variations of illumination, poses and occlusions. The depth acquisition provides an effective way to solve the above issues. In this paper, a complete framework of hand activity recognition combined depth information is presented for fine-motion analysis. First, the improved graph cuts method is introduced to hand location and tracking over time. Then, combined with 3D geometric characteristics and hand behavior prior information, 3D Mesh MoSIFT feature descriptor is proposed to represent the discriminant property of hand activity. Simulation orthogonal matching pursuit (SOMP) is used to encode the visual codewords. Experiments are based on the public available depth datasets (ChaLearn gesture dataset and our captured RGB-D dataset). Compared with the previous popular approaches, our framework has a consistently better performance for real-world applications with fine-motion analysis in terms of effectiveness, robustness and universality.