A DTW gesture recognition system based on gesture orientation histogram

Dynamic time warping algorithm is a pattern matching algorithm that allows a nonlinear stretching of the data. In a recognition system using a matching algorithm, data clustering methods are used to reduce the number of gesture templates in the database, and thus reduce the computational cost; however, the recognition rate is degraded. In this paper, we proposed a DTW gesture recognition system that uses orientation histogram for feature extraction and candidate selection based on weighted probability histogram comparison. Probability histogram is obtained from orientation histogram. Experiment has shown that our method reduces the process time while keeping robustness against accuracy loss due to clustering method.