提出了一种紧密二值描述子用于解决RGB-D 人脸识别过程中的特征表达问题。首先，不同于手工设计的特征，该方法使用无监督学习从训练数据自动获取紧密的二值特征；其次，该方法使用像素与周围像素的差异信息作为输入，利用了空间上下文信息；最后，考虑到Depth 图像平滑性特点，对分块的Depth 和RGB 图提取不同半径范围的像素差异信息。实验结果表明，该方法具有较强的人脸描述能力，且对光照和面部遮挡具有一定的鲁棒性，并在两个公开的RGB-D 数据库上获得了较好的识别率。

A compact binary feature for RGB-D face description and recognition is proposed. First, different from traditional hand-craft feature, we learned the compact binary feature from the training set using unsupervised learning method. Then, in order to make full use of the contextual information, we use the pixel difference vectors as the input. Finally, considering the smoothness of the depth image, we extract different size of pixel difference vectors from every block of RGB and depth image. This work demonstrates that the proposed method is highly discriminable and is robust to facial occlusion and illumination. And recognition rates are comparatively high on two publicly available RGB-D Kinect database.