Fisher Tensor Decomposition for Unconstrained Gait Recognition

This paper proposes a simplified Tucker decomposition of a tensor model for gait recognition from dense local spatiotemporal (S/T) features extracted from gait video sequences. Unlike silhouettes, local S/T features have displayed state-of-art performances on challenging action recognition testbeds, and have the potential to push gait ID towards real-world deployment. We adopt a Fisher representation of S/T features, rearranged as tensors. These tensors still contain redundant information, and are projected onto a lower dimensional space with tensor decomposition. The dimensions of the reduced tensor space can be automatically selected by keeping a proportion of the energy of the original tensor. Gait features can then be extracted from the reduced “core” tensor, and ranked according to how relevant each feature is for classification. We validate our method on the benchmark USF/INIST gait data set, showing performances in line with the best reported results.