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

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