监控场景下人脸图像质量分析技术的研究具有重要意义. 由于监控视频采集到人脸模糊、头部角度不正、被其他物体遮挡等的低质量图像进入识别系统会造成识别准确率下降. 为了解决上述问题, 通过实验, 研究了监控场景下影响图像质量的两个重要因素: 人脸角度、图像清晰度. 设计了基于聚类的人脸图像质量分析算法, 提出了人脸图像质量分数的计算公式, 实验表明该技术能够有效过滤监控视频下采集到的低质量图像, 进而提高人脸识别系统的准确率.
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.