Comparing gesture recognition accuracy using color and depth information

In human-computer interaction applications, gesture recognition has the potential to provide a natural way of communication between humans and machines. The technology is becoming mature enough to be widely available to the public and real-world computer vision applications start to emerge. A typical example of this trend is the gaming industry and the launch of Microsoft's new camera: the Kinect. Other domains, where gesture recognition is needed, include but are not limited to: sign language recognition, virtual reality environments and smart homes. A key challenge for such real-world applications is that they need to operate in complex scenes with cluttered backgrounds, various moving objects and possibly challenging illumination conditions. In this paper we propose a method that accommodates such challenging conditions by detecting the hands using scene depth information from the Kinect. On top of our detector we employ a dynamic programming method for recognizing gestures, namely Dynamic Time Warping (DTW). Our method is translation and scale invariant which is a desirable property for many HCI systems. We have tested the performance of our approach on a digits recognition system. All experimental datasets include hand signed digits gestures but our framework can be generalized to recognize a wider range of gestures.