Fast invariant contour-based classification of hand symbols for HCI
Video-based recognition of hand symbols is a promising technology for designing new interaction techniques for multi-user environments of the future. However, most approaches still lack performance for direct application for human-computer interaction (HCI). In this paper we propose a novel approach to contour-based recognition of hand symbols for HCI. We present adequate methods for normalization and representation of signatures extracted from boundary contours, which allow for efficient recognition of hand poses invariant to translation, rotation, scale and viewpoint variations, which are relevant for many applications in HCI. The developed classification system is evaluated on a dataset containing 13 hand symbols captured from four different persons.