Complex Background Expression Face Detection

Research efforts in face processing include face detection, face tracking, face recognition as well as derivative analysis of pose and expression. Face detection is a key technology of the filed of face information processing. In the beginning, research efforts in face processing chiefly fastened its attention on face recognition, and early face recognition algorithms are based on having a frontal face or faces' getting easily. But with faces' application spreading and practical systems' demand improving, this kind of research on this condition can't satisfy requirements any more. Face detection develops as an alone research.In this dissertation, we first sum up and analyze current typical algorithms on face detection, and then use a color-based method to find faces in the complicated background. This method is discussed on details as follows. First, the algorithm analyzes and compares complexion's clustering in YCrCb color space, and then establishes a skin model based on the color space. Using this model, the complexion is segmented and two-value processed. On account of disturbance's immanence, we can get backup face area using the filter based on mathematical morphology. In the selective and conformed stage, we utilize faces' geometrical characters to roughly choose between these backup areas. Finally we use information of organs' brightness to repeatedly choose and verify these regions, and to get the end results.The algorithm, because of using the skin model that decreases backup regions under the complicated background, is fast. In the last part of the dissertation, we implement the algorithm using C#, and experiment in our face testing set and frontal face dataset of CIT. The results show that our method is robust, and strongly adaptive to variant pose, different illumination and expression. But due to face's complexity by itself, there are some missing faces and false faces. But in a word, hit rate is acceptable. The system is practical to a certain extent.