Hand gesture is one of the most effective methods to perform interactions between humans and also between humans and computers. However, currently existing depth cameras do not provide sufficient resolution and precision for effectively recognizing hand postures in distance (>2 meters). Existing researches tried to solve the limitation by using a combination of depth information and color information. However, they all could not have stable performance, because the color information is naturally affected by visible light condition. In this paper, we introduce a hardware system and an algorithm to recognize hand postures of a distant user while guaranteeing its performance even in the dark. Specifically, by utilizing infrared(IR) lights and their frequency difference, our system simultaneously gathers a depth map from Kinect and a high resolution IR image of a scene from an additional IR camera without any interference. The system analyzes the IR image of a hand using histogram of oriented gradients and support vector machine. In addition, the recognition system has a technique to compensate errors of hand position estimation unavoidable in any hand detection algorithms. As a result, from the experiment on real-time data, the proposed system classifies seven different hand postures with an average precision rate of 92.17% and the precision rate is maintained in the dark (<5 lux) with an average precision rate of 93.28%.

A Gray code and its variants are popular code-patterns for a structured light system. An n-bit Gray code is a kind of binary code whose adjacent code-strings differ only in one bit position. We introduce a specified Gray code, called an 'antipodal Gray code.' Since the n-bit antipodal Gray code has the additional property that the complement of any code-string appears exactly n steps away in the list, the spatial frequency (the width between the white and black stripes) of the antipodal Gray code-pattern is similar along frames. In this paper, we describe the limitations of structured light and the criteria for robust codes. We evaluate the original and antipodal Gray codes, and the experimental results show that the antipodal Gray code provides more robust and accurate results than original Gray codes.