The development of depth cameras, e.g., the Kinect sensor, provides new opportunities for human computer interaction (HCI). Although the Kinect sensor has been extensively applied for human tracking, human action recognition and hand gesture recognition, real time hand gesture recognition is still a challenging problem. In this paper, a new real time hand gesture recognition method is proposed. Since fingers are the most important clue for hand gesture classification, a finger-emphasized multi-scale descriptor is proposed. The proposed descriptor incorporates three types of parameters of multiple scales to make a discriminative representation of the hand shape. Furthermore, the features of fingers are emphasized for hand gesture analysis. Three solutions to hand gesture recognition are then investigated with DTW, SVM, and neural network. Extensive experiments are conducted and the results show that the proposed method is robust to noise, articulations and rigid transformations. The comparison with state-of-the-art methods verifies the accuracy and efficiency of our method.

深度相机（例如Kinect传感器）的发展为人机交互（HCI）提供了新的机会。尽管Kinect传感器已广泛应用于人体跟踪，人体动作识别和手势识别，但是实时手势识别仍然是一个具有挑战性的问题。本文提出了一种新的实时手势识别方法。由于手指是手势分类的最重要线索，因此提出了一种手指强调的多尺度描述符。所提出的描述符结合了多种比例尺的三种类型的参数，以区分手形。此外，手指的特征被强调用于手势分析。然后使用DTW，SVM和神经网络研究了手势识别的三种解决方案。进行了广泛的实验，结果表明该方法对噪声，清晰度和刚性变换具有鲁棒性。与最新方法的比较证明了我们方法的准确性和效率。