Parallel Hand Shape Classification

This paper introduces a new parallel algorithm (PA) for fast hand shape classification. This problem is challenging as a hand is characterized by a high number of degrees of freedom. Our objective is to design and implement a robust algorithm suitable for real-time applications. We show how the analysis time can be decreased, together with the increase of the classification accuracy, by the means of parallelization. Also, we propose to combine the shape contexts approach with the appearance-based techniques to increase the efficacy of the PA. An extensive experimental study confirms the effectiveness of the proposed PA compared with other state-of-the-art methods.