Fast and Accurate Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel, real-time framework for fast hand shape classification. We show how the number of gallery images influences the classification accuracy and execution time of the algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Different methods can be used at each step of the proposed parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.