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

[2608.14670] ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14670 (cs) [Submitted on 2 Aug 2026] Title: ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry Authors: Nayan Sanjay Bhatia , Pranay Kocheta , Yuhan Li , Katia Obraczka View a PDF of the paper titled ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry, by Nayan Sanjay Bhatia and 3 other authors View PDF HTML (experimental) Abstract: 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 \emph{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 \emph{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 $78.88\% \pm 1.62\%$ Top-1 accuracy on 6-second windows and $84.85\% \pm 1.31\%$ 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 $4.4\times$ 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 $27\times$ 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. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14670 [cs.LG] (or arXiv:2608.14670v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14670 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Nayan Sanjay Bhatia [ view email ] [v1] Sun, 2 Aug 2026 19:06:16 UTC (1,083 KB) Full-text links: Access Paper: View a PDF of the paper titled ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry, by Nayan Sanjay Bhatia and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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