The research on quality analysis technology for face images in surveillance scenes is of great significance. Since the low-quality images collected from surveillance videos have blurred faces and incorrect head angles and are subjected to occlusion by other objects, the input of them into the recognition system can result in lower identification accuracy of the system. To solve the above problems, this work studies two important factors that affect image quality in surveillance scenes through experiments, namely, the face angle and image clarity. Thus, a quality analysis algorithm for face images based on clustering is designed, and a calculation method for scoring the quality of face images is proposed. The experiment proves that the technology can effectively filter the low-quality images collected in the surveillance videos and improve the accuracy of the face recognition system.