Orthogonal Hankel Subspaces for Applications in Gesture Recognition

Abstract

Gesture recognition is an important research area in video analysis and computer vision. Gesture recognition systems include several advantages, such as the interaction with machines without needing additional external devices. Moreover, gesture recognition involves many challenges, as the distribution of a specific gesture largely varies depending on viewpoints due to its multiple joint structures. In this paper, We present a novel framework for gesture recognition. The novelty of the proposed framework lies in three aspects: first, we propose a new gesture representation based on a compact trajectory matrix, which preserves spatial and temporal information. We understand that not all images of a gesture video are useful for the recognition task, therefore it is necessary to create a method where it is possible to detect the images that do not contribute to the recognition task, decreasing the computational cost of the overall framework. Second, we represent this compact trajectory matrix as a subspace, achieving discriminative information, as the trajectory matrices obtained from different gestures generate dissimilar clusters in a low dimension space. Finally, we introduce an automatic procedure to infer the optimal dimension of each gesture subspace. We show that our compact representation presents practical and theoretical advantages, such as compact representation and low computational requirements. We demonstrate the advantages of the proposed method by experimentation employing Cambridge gesture and Human-Computer Interaction datasets.