Enhanced gait recognition based on weighted dynamic feature

Gait Energy Image (GEI) has been shown to be a robust gait descriptor for gait recognition, and many algorithms based on GEI have been proposed. We propose in this paper an improved algorithm to exploit the discriminative information of GEI in identifying walking people based on gait sequences. Specifically, we first obtain the discriminative power of each pixel in the GEI, referred to as feature weight or feature score, through statistic learning from the whole gallery set. We then generate a binary mask for each frame in a gait sequence according to the intensity value of the GEI to separate the dynamic part from static part of GEI. Combining the feature score and the binary mask, we arrive at a new feature for every GEI for discriminative representation and effective recognition. Experimental results on both NLPR and USF databases show the effectiveness of our proposed algorithm in terms of gait recognition rate.