Gait recognition is nowadays an important biometric
technique for video surveillance tasks, due to the advantage of
using it at distance. However, when the upper body movements
are unrelated to the natural dynamic of the gait, caused for
example by carrying a bag or wearing a coat, the reported results
show low accuracy. With the goal of solving this problem, we
apply persistent homology to extract topological features from
the lowest fourth part of the body silhouettes. To obtain the
features, we modify our previous algorithm for gait recognition,
to improve its efficacy and robustness to variations in the amount
of simplices of the gait complex. We evaluate our approach
using the CASIA-B dataset, obtaining a considerable accuracy
improvement of 93:8%, achieving at the same time invariance to
upper body movements unrelated with the dynamic of the gait.Ministerio de Economía y Competitividad MTM2015-67072-
