Gesture Recognition Performance Score: A New Metric to Evaluate Gesture Recognition Systems

Abstract

Abstract. In spite of many choices available for gesture recognition algorithms, the selection of a proper algorithm for a specific application remains a difficult task. The available algorithms have different strengths and weaknesses making the matching between algorithms and applications complex. Accurate evaluation of the performance of a gesture recognition algorithm is a cumbersome task. Performance evaluation by recognition accuracy alone is not sufficient to predict its successful realworld implementation. We developed a novel Gesture Recognition Performance Score (GRP S) for ranking gesture recognition algorithms, and to predict the success of these algorithms in real-world scenarios. The GRP S is calculated by considering different attributes of the algorithm, the evaluation methodology adopted, and the quality of dataset used for testing. The GRP S calculation is illustrated and applied on a set of vision based hand/ arm gesture recognition algorithms reported in the last 15 years. Based on GRP S a ranking of hand gesture recognition algorithms is provided. The paper also presents an evaluation metric namely Gesture Dataset Score (GDS) to quantify the quality of gesture databases. The GRP S calculator and results are made publicly available (http://software.ihpc.a-star.edu.sg/grps/)