The small sample size problem and the difficulty in determining the optimal reduced dimension limit the application of subspace learning methods in the gait recognition domain. To address the two issues, we propose a novel algorithm named
multi-linear tensor-based learning without tuning parameters (
for gait recognition. In
, we first employ a new method for automatic selection of the optimal reduced dimension. Then, to avoid the small sample size problem, we use multi-linear tensor projections in which the dimensions of all the subspaces are automatically tuned. Theoretical analysis of the algorithm shows that
converges. Experiments on the USF Human Gait Database show promising results of
compared to other gait recognition methods.