Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification


  • Lan Wei Peking University
  • Yonghong Tian Peking University
  • Yaowei Wang Beijing Institute of Technology
  • Tiejun Huang Peking University



Person re-identification, Gait recognition, Cascade ranking, Swiss system


Human gait has been shown to be an efficient biometric measure for person identification at a distance. However, it often needs different gait features to handle various covariate conditions including viewing angles, walking speed, carrying an object and wearing different types of shoes. In order to improve the robustness of gait-based person re-identification on such multi-covariate conditions, a novel Swiss-system based cascade ranking model is proposed in this paper. Since the ranking model is able to learn a subspace where the potential true match is given the highest ranking, we formulate the gait-based person re-identification as a bipartite ranking problem and utilize it as an effective way for multi-feature ensemble learning. Then a Swiss multi-round competition system is developed for the cascade ranking model to optimize its effectiveness and efficiency. Extensive experiments on three indoor and outdoor public datasets demonstrate that our model outperforms several state-of-the-art methods remarkably.




How to Cite

Wei, L., Tian, Y., Wang, Y., & Huang, T. (2015). Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Machine Learning Applications