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摘要
This paper presents a robust and real-time hand posture recognition system. To obtain this, key elements of the proposed system contain an user-guide scheme and a kernel-based hand posture representation. We firstly describe a three-stage scheme to train an end-user. This scheme aims to adapt environmental conditions (e.g., background images, distance from device to hand/human body) as well as to learn appearance-based features such as hand-skin color. Thanks to the proposed user-guide scheme, we could precisely estimate heuristic parameters which play an important role for detecting and segmenting hand regions. Based on the segmented hand regions, we utilize a kernel-based hand representation in which three levels of feature are extracted. Whereas pixel-level and patch-level are conventional extractions, we construct image-level which presents a hand pyramid structure. These representations contribute to a Multi-class support vector machine classifier. We evaluate the proposed system in term of the learning time versus the robustness and real time performances. Averagely, the proposed system requires 14 s in advanced to guide an end-user. However, the hand posture recognition rate obtains 91.2% accuracy. Performance of the proposed system is comparable with state-of-the-art methods (e.g. Pisharady et al., 2012) but it is a real time system. To recognize a posture, its computational cost is only 0.15 s. This is significantly faster than works in Pisharady et al. (2012), which required approximately 2 min. The proposed methods therefore are feasible to embed into smart devices, particularly, consumer electronics in domain of home-automation such as televisions, game consoles, or lighting systems.
- 出版日期2016-3
- 单位华中科技大学
