Uncooperative gait recognition by learning to rank
We formulate transfer learning based on a bipartite ranking model for gait recognition. Invariant features to covariate conditions are transferred across different people. Under-sampled training data is handled by leveraging samples of different people. A single model can deal with any covariate condition and combinations of them. Outperforming other methods especially under challenging uncooperative settings.
gait recognition learning to rank transfer learning covariate conditions distance learning