Probabilistic face recognition from compressed imagery

The effects of image and video compression on face recognition in the still-to-video setting are studied in this paper. We use the probabilistic framework described in (S. Zhou et al., Computer Vision and Image Understanding, vol.91, p.214-245, 2003), which solves tracking and recognition problems simultaneously via sequential importance sampling (SIS). To account for the illumination and pose variations in test sequences, intrapersonal space (IPS) is constructed from exemplary views and used to calculate the likelihood density. Both the gallery images and probe videos are compressed and several experiments are run to study their effects on the recognition rate. Some useful conclusions are drawn from the analysis of the experimental results, which are helpful for future research on the interaction between recognition and compression. Meanwhile, the experiments also demonstrate the robustness of the proposed methods.