Hankel subspace method for efficient gesture representation

Abstract

Gesture recognition technology provides multiple opportunities for direct human-computer interaction, without the use of additional external devices. As such, it had been an appealing research area in the field of computer vision. Many of its challenges are related to the complexity of human gestures, which may produce nonlinear distributions under different viewpoints. In this paper, we introduce a novel framework for gesture recognition, which achieves high discrimination of spatial and temporal information while significantly decreasing the computational cost. The proposed method consists of four stages. First, we generate an ordered subset of images from a gesture video, filtering out those that do not contribute to the recognition task. Second, we express spatial and temporal gesture information in a compact trajectory matrix. Then, we represent the obtained matrix as a subspace, achieving discriminative information, as the trajectory matrices derived from different gestures generate dissimilar clusters in a low dimension space. Finally, we apply soft weights to find the optimal dimension of each gesture subspace. We demonstrate practical and theoretical gains of our compact representation through experimental evaluation using two publicity available gesture datasets.