Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, fueling the automatic gait recognition field. Whereas, gait recognition works usually focus on improving end-to-end performance measures, and this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. The assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject identification scenario. In this scenario, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and differences between subjects in a verification setup. The explorations concluded that periodic components occurring in frequencies between 6Hz and 10Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.