Gait recognition using periodic temporal super resolution for low frame-rate videos

This paper describes a method of gait recognition where both a gallery and a probe are based on low frame-rate videos. The sparsity of phases (stances) per gait period makes it much harder to match the gait using existing gait recognition algorithms. Consequently, we introduce a super resolution technique to generate a high frame-rate periodic image sequence as a preprocess to matching. First, the initial phase for each frame is estimated based on an exemplar of a high frame-rate gait image sequence. Images between a pair of adjacent frames sorted by the estimated phases are then filled using a morphing technique to avoid ghosting effects. Next, a manifold of the periodic gait image sequence is reconstructed based on the estimated phase and morphed images. Finally, the phase estimation and manifold reconstruction are iterated to generate better high frame-rate images in the energy minimization framework. Experiments with real data on 100 subjects demonstrate the effectiveness of the proposed method particularly for low frame-rate videos of less than 5 fps.