Curvature filtering-empirical mode decomposition preprocessing was used to detect and extract human target features. It reduced the computational complexity of image decomposition, enhanced edge and texture features simultaneously, and improved feature differentiation. Its performance was in the following areas: (1) In the first layer of empirical mode decomposition, the continuous smooth surface of the original image was mapped by curvature filtering plane to form the envelope surface and the mean surface. The first layer extracted texture features, and the following layers highlighted structure features. (2) Matching regions had sharp edges from adjacent layers which varied from low-resolution to high-resolution, easy to extract the target contour candidate regions. (3) Decomposing the texture features in the first floor to screen the background, matching the foreground feature areas in the adjacent layers to form the trajectory map of the human body posture, it made easy judging the human body posture and behavior. When applying it to human behavior recognition experiments, and comparing it with the ground truth of the human behavior database, the accuracy of the contour extraction and the recall rate of the UIUC sample data were all over 90%. And experiments verify the preprocessing method is effective in human pose recognition and behavior recognition.