Identifying Face Quality and Factor Measures for Video

This paper identifies important factors for face recognition algorithm performance in video. The goal of this study is to understand key factors that affect algorithm performance and to characterize the algorithm performance. We evaluate four factor metrics for a single video as well as two comparative metrics for pairs of videos. This study carried out an investigation of the effect of nine factors on three algorithms using the Point-and-Shoot Challenge (PaSC) video dataset. These factors can be categorized into three groups: 1) image/video (pose yaw, pose roll, face size, and face detection confidence); 2) environment (environmental condition with person’s activity and sensor model); and 3) subject (subject ID, gender, and race). For videobased face recognition, the analysis shows that the distribution-based methods were generally more effective in quantifying factor values. For predicting face recognition performance in a video, we observed that face detection confidence and face size serve as potentially useful quality measure metrics. We also find that male faces are easier to identify than female faces, and Asians are easier than Caucasians. Further, on the PaSC video dataset, the performance of face recognition algorithms are primarily driven by environment and sensor factors.