On rank aggregation for face recognition from videos

Face recognition from still face images suffers due to intrapersonal variations caused by pose, illumination, and expression that degrade the performance. On the other hand, videos provide abundant information that can be leveraged to compensate the limitations of still face images and enhance face recognition performance. This paper presents a video based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images. The video signature embeds diverse intra-personal and temporal variations across multiple frames, thus facilitates matching two videos with large variations. Two videos are matched by comparing their discriminative signatures using the Kendall tau similarity distance measure. Performance comparison with the benchmark results and a commercial face recognition system on the publicly available YouTube faces database show the efficacy of the proposed video based face recognition algorithm.