Person Identification and Imposter Detection using Footstep generated Seismic Signals

In this work, we propose a novel, unconstrained biometric authentication system that utilizes footstep generated seismic waves. It has the following advantages over existing systems: 1) it does not require any special orientation or positioning of the individual for authentication and 2) seismic data do not interfere with the individual’s privacy. We curate an indigenous data set containing ~78 000 footstep events collected from 8 individuals in three types of surfaces (concrete tile floor, carpet floor, and wooden floor) to validate the robustness of the proposed system. It is also capable of detecting unauthorized/unregistered individuals (imposters). To mimic realistic scenarios for imposter detection, the models are trained only with the footstep data of registered users (footsteps of unregistered users are completely unknown/unseen to the trained models), thus achieving imposter detection for footstep-based person identification. We achieved an accuracy of 90% (in a concrete tile floor and wooden floor) and 94% (in carpet floor) for person identification by using features from as low as two consecutive footsteps. For imposter detection, we achieved an accuracy of 76%–80% (in a concrete tile floor and wooden floor) and 87% (in carpet floor) by utilizing features from 10 consecutive footsteps.