Abstract: As one of the most promising natural Human Computer Interaction（HCI） technology, gesture recognition has successfully applied in several fields. However, most of the dependable methods of gesture recognition depend on specialized hardware. The popularization of the natural HCI technology needs universal gesture recognition method based on universal camera. Therefore, in this paper, the static gesture segmentation and recognition method based on universal camera is researched, and gesture images are captured under different complex background and illuminations. First, an 8-neighbour based transformation algorithm is applied to overcome the impact to the segmentation in different illuminations. Second, an algorithm named Least Average Hausdorff Distance Area（LAHDA） is proposed to overcome the interference of the segmentation in different gesture shape, directions and scales. The simulation results show that this algorithm works well in gesture segmentation under different complex background and illuminations, the correct segmentation rate of the testing samples can be as high as 99.8%. At last, the Sequential Minimal Optimization（SMO） algorithm is improved to train the Binary Tree Support Vector Machine（BT-SVM） multi-classifier, and the average recognition rate of the system is up to 80% on the testing set which is built in this experiment. The experimental results show that the proposed method is a promise method which can be used as a universal HCI mode based on one common camera.

Key words: Hausdorff distance, Support Vector Machine, gesture segmentation, gesture recognition, Sequential Minimal Optimization

摘要： 手势识别技术作为最有前景的一种自然人机交互模式已经成功应用于一些领域。可靠的手势识别技术多依赖特定的硬件实现，而这种自然交互模式的普及需要自然环境下基于普通摄像机的通用手势识别技术。研究了在普通摄像机下对各种复杂背景、不同光照条件的静态手势的分割和识别技术。首先采用一种邻域变换算法，克服不同光照强度对分割的影响，然后提出一种求最小平均Hausdorff距离区域的算法，克服不同手势形状、方向、尺度等对分割的干扰。手势分割实验结果证明提出的算法可以在各种复杂背景及不同光照条件下分割出手势区域，正确率达到99.8%。最后改进了序贯最小优化算法训练二叉树结构的支持向量机多分类器，对实验采集的各种自然条件下九类手势图像的平均识别率超过80%，证明了算法用作普通摄像机下通用人机交互模式的可行性。

关键词: Hausdorff距离, 支持向量机, 手势识别, 手势分割, 序贯最小优化