ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry

Passive, device-free person identification offers an alternative to camera- and wearable-based biometrics, yet existing wireless approaches rely largely on gait or activity cues and are rarely evaluated at scale. In this paper, we present Argus, a passive Wi-Fi sensing system that identifies people from commodity Channel State Information (CSI) without requiring an attached device or a prescribed motion. Argus converts short CSI spans into compact statgrams: statistical maps built from the channel views available on a given device. A lightweight decoder-only Transformer then reads coarse statgram patches as tokens, and segment-level logit aggregation combines evidence over time. On a 154-subject CSI dataset evaluated with a strict physical-segment split, Argus reaches 78.88% ± 1.62% Top-1 accuracy on 6-second windows and 84.85% ± 1.31% after aggregating 19 overlapping windows over a 60-second segment; Top-3 and Top-5 reach 98.61% and 99.26%. For a 60-second statgram, Argus improves over a raw-CSI Transformer baseline by 7.75 points while using 4.4× fewer FLOPs per window. Attention-guided compression preserves full single-window accuracy with only half of the EHealth patches. On WiMANS, a multi-user benchmark across three rooms and two Wi-Fi bands, Argus remains within 1.23 percentage points of the strongest per-configuration baselines on average while using 27× fewer inference FLOPs. These results show that compact CSI statistics can scale passive identification while also exposing deployment limits in open-set rejection and cross-room transfer.