Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories

Particularities in the individuals' style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition field. Whereas gait recognition works usually focus on improving end-to-end performance measures, 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. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identification scenario. In this setup, 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 6 and 10 Hz are more significant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.