Gait regeneration for recognition

Gait recognition has potential to recognize subject in CCTV footages thanks to robustness against image resolution. In the CCTV footage, several body-regions of subjects are, however, often un-observable because of occlusions and/or cutting off caused by limited field of view, and therefore, recognition must be done from a pair of partially observed data. The most popular approach to recognition from partially observed data is matching the data from common observable region. This approach, however, cannot be applied in the cases where the matching pair has no common observable region. We therefore, propose an approach to enable recognition even from the pair with no common observable region. In the proposed approach, we reconstruct entire gait feature from a partial gait feature extracted from the observable region using a subspace-based method, and match the reconstructed entire gait features for recognition. We evaluate the proposed approach against two different datasets. In the best case, the proposed approach achieves recognition accuracy with EER of 16.2% from such a pair.