Face recognition methods are primarily designed for single-image analysis, even though video-based recognition has seen a dramatic increase in popularity in edge security and surveillance applications. Typically, a video template is constructed from the features of individual frames. Feature norms are commonly used as weights in the construction process, as they correlate well with the usefulness of samples for recognition. Classical training approaches directly optimize only the angular distances, in turn also guiding the feature norms. This can lead to suboptimal alignment between feature norms and the usefulness (utility) of samples, resulting in subpar video performance. Motivated by this insight, we propose the UVFace methodology, which presents an extended feature norm alignment branch. Through careful design of the quality ranking step, which produces feature norm labels and a new feature norm loss, UVFace improves performance over the reproduced AdaFace baseline on video-oriented benchmarks while retaining strong image-based performance. Code is available at
https://github.com/LSIbabnikz/UVFace
