中文摘要：

为实现复杂背景下的手势识别,根据手势图像的区域形状特征提出一种基于手势空间分布特征的手势识别算法.利用复杂背景下基于亮度高斯模型的手势分割算法分割出肤色区域,利用＂搜索窗口＂筛选当前肤色区域实现手势定位,并提取包括空间相对密度特征和指节相对间距特征的手势空间分布特征,最后综合手势的2个手势特征向量计算总的相似性来识别手势.通过引入随机采样机制提高识别速度,并引入搜索窗口机制实现肤色干扰时的手势识别.实验结果表明,在环境光照相对稳定的条件下,文中算法能够实现鲁棒的实时手势识别,且具有很好的旋转、平移、缩放不变性,对于差异较大的手势识别率高达98%.

英文摘要：

A hand-distribution-features-based approach to hand gesture recognition is presented in this paper.Firstly,a segmentation scheme for images under complex background is proposed which is based on the proposed Brightness-Gauss-Model.Then,search-window is used to select the valid hand gesture and extract the hand distribution features,which are composed of the destiny distribution feature and the figures features.Finally,we integrated all the features to calculate similarity distance.In this paper,random sampling is introduced which can improve recognition rate.Even if there are the hand and the face in our Video Sequence Images,we can still recognize the hand gestures because of the using of search window.The experimental results show that if the ambient light is relatively stable,the algorithm proposed in this paper can recognize hand gestures in real-time with strong robustness,and it is invariant to rotation,scale and translation.For those gestures with large differences,the recognition rate is up to 98%.