PURE: Passive mUlti-peRson idEntification via Deep Footstep Separation and Recognition

Recently, textitpassive behavioral biometrics (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the same time. To this end, we propose systemname as a passive multi-person identification system leveraging deep learning enabled footstep separation and recognition. systemname passively identifies a user by deciphering the unique "footprints" in its footstep. Different from existing gait-enabled recognition systems incurring a long sensing delay to acquire many footsteps, systemname can recognize a person by as few as only one step, substantially cutting the identification latency. To make systemname adaptive to walking pace variations, environmental dynamics, and even unseen targets, we apply an adversarial learning technique to improve its domain generalisability and identification accuracy. Finally, systemname can defend itself against replay attack, enabled by the richness of footstep and spatial awareness. We implement a systemname prototype using commodity hardware and evaluate it in typical indoor settings. Evaluation results demonstrate a cross-domain identification accuracy of over 90%.