Speed Invariance vs. Stability: Cross-Speed Gait Recognition Using Single-Support Gait Energy Image

Gait recognition has recently attracted much attention since it can identify person at a distance without subject cooperation. Walking speed changes, however, cause gait changes in appearance, which significantly drops performance of gait recognition. Considering a speedinvariant property at single-support phases where stride change due to speed changes are mitigated, and a stability against phase estimation error and segmentation noise by aggregating multiple phases inspired by gait energy image (GEI), we propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good tradeoff between the speed invariance and the stability by combining singlesupport phases and GEI concept. For this purpose, we firstly find out the optimal duration around single support phases using a training set so as to well balance the speed invariance and the stability. We then extract SSGEI by aggregating multiple single-support frames. Finally, we combine the proposed SSGEI with subsequent Gabor filters and metric learning for better performance. Experiments on the publicly available OU-ISIR Treadmill Dataset A composed of the largest speed variations demonstrated that the proposed method yielded 99.33% rank-1 identification rate on average for cross-speed gait recognition, which outperforms the other state-of-the-arts, and realized a low computational cost as well.