手脸近距遮挡属于深度传感器应用中具有代表性的难点问题，针对该问题提出了一种综合利用颜色与深度信息的手势识别方法。采用核模糊C-均值聚类，对手脸遮挡图像进行粗分割和灰度增强，实现手脸分离。引入初始化水平集函数，解决聚类方法导致的手势区域像元缺失问题。利用基于深度信息的梯度方向直方图(HOG) 特征对手势进行分类识别。通过采集不同人体手脸近距遮挡情形下的多种手势图像建立了样本数据库，进行了对比实验，实验结果验证了该方法的可行性和有效性。本文方法能有效分离近距遮挡的手和脸，提取得到相对完整的手势信息，深度HOG特征能够对手势空间信息进行精确描述，具有比传统形状特征更准确的识别效果。

Hand over face occlusion is a typical difficulty during depth sensor application. Aiming at this point, a gesture recognition algorithm is proposed using color and depth information comprehensively. Kernel fuzzy C-means algorithm is used to get rough segments of hand over face occlusion image and gray enhancement. The hand face separation is achieved. The initial level set function is introduced to solve the clustering method of gesture area pixels missing problem. The classification of gestures is carried based on the histogram of oriented gradient (HOG) features of depth map. The sample database is established by collecting different human hand and face close covered case gesture images. Comparative result proves the feasibility and effectiveness of the proposed algorithm. The proposed method can separate hand from face efficiently and get relatively ideal result, and the HOG features can describe the spatial information more precisely and get higher recognition rate compared with traditional characteristics.