Human identification is the task of determining the identity of a specific individual from a set of known subjects using WiFi channel state information, without requiring the person to carry any device or interact with dedicated hardware. It matters to the field because it enables passive, contactless authentication and surveillance applications that complement or replace traditional biometric systems, leveraging the fact that each person's unique physical characteristics and gait produce distinctive multipath disturbances in CSI signals. Key variants include closed-set identification, where the subject must belong to a predefined group, and open-set identification, which additionally requires detecting unknown individuals not seen during training, with both variants often evaluated across different environmental conditions to assess cross-scenario generalizability.
Source Papers
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing ↗ — CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing
- Device-Free Passive Identity Identification via WiFi Signals ↗ — Device-Free Passive Identity Identification via WiFi Signals
- SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing ↗ — SenseFi: A library and benchmark on deep-learning-empowered
- Towards Environment Independent Device Free Human Activity Recognition ↗ — Towards Environment Independent Device Free Human Activity R
- WiFi CSI-based device-free sensing: from Fresnel zone model to CSI-ratio model ↗ — WiFi CSI-based device-free sensing: from Fresnel zone model
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information