Generalizing face quality and factor measures to video

Methods for assessing the impact of factors and image-quality metrics for still face images are well-understood. The extension of these factors and quality measures to faces in video has not, however, been explored. We present a specific methodology for carrying out this extension from still to video. Using the Point-and-Shoot Challenge (PaSC) dataset, our study investigates the effect of nine factors on three face recognition algorithms, and identifies the most important factors for algorithm performance in video. We also evaluate four factor metrics for characterizing a single video as well as two comparative metrics for pairs of videos. For video-based face recognition, the analysis shows that distribution-based metrics are generally more effective in quantifying factor values than algorithm-dependent metrics. For predicting face recognition performance in video, we observe that the face detection confidence and face size factors are potentially useful quality measures. From our data, we also find that males are easier to identify than females, and Asians easier to identify than Caucasians. Finally, for this PaSC video dataset, face recognition algorithm performance is primarily driven by environment and sensor factors.