  Recently, \textit{passive 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\%.
