Combing KNN with LCSS for dynamic hand gesture recognition
Journal: Journal of Computational Information Systems
We propose a dynamic hand gesture recognition system based on Nguyen-Dinh's longest common subsequence (LCSS) based method, and improve the recognition accuracy from two aspects. First, in order to provide discrete features which characterize gesture movement for LCSS, this paper presents a spherical direction discretization (SDD) method to encode gesture features which can show the differences of each gesture. Second, taking into account different people have different speeds, trajectories and spatial positions to perform the same gesture, this paper combines K nearest neighbors (KNN) with the LCSS to solve the different habits of users. The experiment results show that SDD+KNN+LCSS outperformed k-means+LCSS by 13: 45% (F1-score).