Face image optimization has important significance for face recognition in intelligent monitoring system. In the case of multi-face tracking in video, there are problems such as tracking error and inability to add and cancel the tracker in time. This paper proposes a face clustering method replacing the face tracking method to obtain face images of the same person, and a face image quality evaluation method to select a face image with good face pose and good image quality from a large number of multi-pose face images of the same person. First, the face detection from the video frame is performed, and then the residual network is used to extract the facial features for face clustering. Finally, the normalized mean value is computed as the weight coefficient of corresponding evaluation index for each type of face after clustering. Consequently, a comprehensive evaluation index is constructed to optimize the face image. Experiments show that face clustering can effectively obtain the same face image, and the constructed face image quality comprehensive evaluation index can effectively select a better face image from the same face images.