Per-pixel hand detection plays an important role in many human–computer interaction applications while accurate and robust hand detection remains a challenging task due to the large appearance variance of hands in images. We introduce a per-pixel hand detection system using one single depth image. We propose a circle sampling depth-context feature for hand regions representation, and a multilayered hand detection model is built for hand regions detection. Finally, a postprocessing step based on spatial constraints is applied to refine the detection results and further improve the detection accuracy. We evaluate the accuracy of our method on a public dataset and investigate the effect of key parameters in our system. The results of the qualitative and quantitative evaluation reveal that the proposed method performs well on per-pixel hand detection tasks. Furthermore, an additional experiment on hand parts segmentation proves that the depth-context feature has a generalization power for more complex multiclass classification tasks.