Hand Recognition Using Depth Cameras

Abstract

Hand position and gesture recognition from a series of images is a topic of relevance for the development of human-machine interaction. The advent of low-cost consumer devices, such as Microsoft Kinect, leaves open the possibility of creating recognition applications that are not affected by low-light conditions. This paper is a survey of the literature on hand position and gesture recognition with the use of depth cameras. Most studies noticeably focus on the recognition of one-handed gestures and their classification within a pre-established set of gestures. Only in research from recent years does one see significant advances in the identification of unconstrained hand poses, that is, inference from the skeleton with the use of depth and color information. Nevertheless, the lack of a standardized set of tests and the diversity of hardware leaves unclear the extent to which these would prove effective with low-cost hardware.