A Gender Recognition Experiment on the CASIA Gait Database Dealing with Its Imbalanced Nature

The CASIA Gait Database is one of the most used benchmarks for gait analysis among the few non-smallsize datasets available. It is composed of gait sequences of 124 subjects, which are unequally distributed, comprising 31 women and 93 men. This imbalanced situation could correspond to some real contexts where men are in the majority, for example, a sports stadium or a factory. Learning from imbalanced scenarios usually requires suitable methodologies and performance metrics capable of managing and explaining biased results. Nevertheless, most of the reported experiments using the CASIA Gait Database in gender recognition tasks limit their analysis to global results obtained from reduced subsets, thus avoiding having to deal with the original setting. This paper uses a methodology to gain an insight into the discriminative capacity of the whole CASIA Gait Database for gender recognition under its imbalanced condition. The classification results are expected to be more reliable than those reported in previous papers.