Gabor phase embedding of Gait Energy Image for identity recognition

This paper proposes an effective gait recognition approach based on the Gait Energy Image (GEI) representation. Synthetic GEI samples are first generated to address the problem of lacking training data. Then the Gabor phase spectrum of GEI which was ignored in the previous work is utilized as the input feature, and it is subsequently embedded into a low dimensional manifold by locality preserving projections to perform classification. The proposed recognition approach is evaluated on the USF HumanID database. Experimental results show that our approach outperforms other automatic algorithms in terms of recognition accuracy.