KHÔNG GIAN ĐA TẠP CỦA CỬ CHỈ ĐỘNG BÀN TAY TRÊN CÁC GÓC NHÌN KHÁC NHAU

Abstract

Recently, a number of methods for dynamic hand gesture recognition has been proposed. However, deployment of such methods in a practical application still has to face with many challenges due to the variation of view point, complex background or subject style. In this work, we deeply investigate performance of hand designed features to represent manifolds for a specific case of hand gestures and evaluate how robust it is to above variations. To this end, we adopt an concatenate features from different viewpoints to obtain very competitive accuracy. To evaluate the robustness of the method, we design carefully a multi-view dataset that composes of five dynamic hand gestures in indoor environment with complex background. Experiments with single or cross view on this dataset show that background and viewpoint has strong impact on recognition robustness. In addition, the proposed method's performances are mostly increased by multi-features combination that its results are compared with Convolution Neuronal Network method, respectively. This analysis helps to make recommendation for deploying the method in real situation.