WiFi-ID: Human identification using WiFi signal
Prior research has shown the potential of devicefree WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual’s gait will...
| Main Authors: | , , , |
|---|---|
| Format: | Conference item |
| Published: |
IEEE
2016
|
| _version_ | 1797077705163800576 |
|---|---|
| author | Zhang, J Wei, B Hu, W Kenhere, S |
| author_facet | Zhang, J Wei, B Hu, W Kenhere, S |
| author_sort | Zhang, J |
| collection | OXFORD |
| description | Prior research has shown the potential of devicefree WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual’s gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings. |
| first_indexed | 2024-03-07T00:21:49Z |
| format | Conference item |
| id | oxford-uuid:7cce80ac-a417-4561-b7bf-9f43dc3634e8 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:21:49Z |
| publishDate | 2016 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:7cce80ac-a417-4561-b7bf-9f43dc3634e82022-03-26T20:59:24ZWiFi-ID: Human identification using WiFi signalConference itemhttp://purl.org/coar/resource_type/c_5794uuid:7cce80ac-a417-4561-b7bf-9f43dc3634e8Symplectic Elements at OxfordIEEE2016Zhang, JWei, BHu, WKenhere, SPrior research has shown the potential of devicefree WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual’s gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings. |
| spellingShingle | Zhang, J Wei, B Hu, W Kenhere, S WiFi-ID: Human identification using WiFi signal |
| title | WiFi-ID: Human identification using WiFi signal |
| title_full | WiFi-ID: Human identification using WiFi signal |
| title_fullStr | WiFi-ID: Human identification using WiFi signal |
| title_full_unstemmed | WiFi-ID: Human identification using WiFi signal |
| title_short | WiFi-ID: Human identification using WiFi signal |
| title_sort | wifi id human identification using wifi signal |
| work_keys_str_mv | AT zhangj wifiidhumanidentificationusingwifisignal AT weib wifiidhumanidentificationusingwifisignal AT huw wifiidhumanidentificationusingwifisignal AT kenheres wifiidhumanidentificationusingwifisignal |