TY - GEN
T1 - Touchless Biometric User Authentication Using ESP32 WiFi Module
AU - Makwana, Rikesh
AU - Shaikh, Talal
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022/4/21
Y1 - 2022/4/21
N2 - Due to the ubiquitous nature of WiFi, the use of WiFi signals for Biometric User Authentication (BUA) is ongoing research which has previously focused on using multi-antenna commercial off-the-shelf (COTS) devices such as Intel 5300 or Atheros 9390. However, due to high cost and limited availability, COTS devices are restricted to small scale deployment. To overcome this issue, researchers propose using Espressif ESP32, an inexpensive single antenna microcontroller equipped with WiFi and Bluetooth modules capable of capturing detailed WiFi Channel State Information (CSI). This paper explores and extends the application of ESP32 by proposing a model for device-less and touch-less BUA systems using a simple client–server architecture. The system identifies users as they perform day-to-day activities by recognizing behavioural and physiological characteristics using LSTM—a deep learning approach. Furthermore, the paper describes the Python tool developed for parsing and filtering WiFi CSI data.
AB - Due to the ubiquitous nature of WiFi, the use of WiFi signals for Biometric User Authentication (BUA) is ongoing research which has previously focused on using multi-antenna commercial off-the-shelf (COTS) devices such as Intel 5300 or Atheros 9390. However, due to high cost and limited availability, COTS devices are restricted to small scale deployment. To overcome this issue, researchers propose using Espressif ESP32, an inexpensive single antenna microcontroller equipped with WiFi and Bluetooth modules capable of capturing detailed WiFi Channel State Information (CSI). This paper explores and extends the application of ESP32 by proposing a model for device-less and touch-less BUA systems using a simple client–server architecture. The system identifies users as they perform day-to-day activities by recognizing behavioural and physiological characteristics using LSTM—a deep learning approach. Furthermore, the paper describes the Python tool developed for parsing and filtering WiFi CSI data.
KW - Biometric user authentication
KW - Channel state information
KW - ESP32 microcontroller
KW - Ubiquitous computing
UR - http://www.scopus.com/inward/record.url?scp=85129231683&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-7618-5_46
DO - 10.1007/978-981-16-7618-5_46
M3 - Conference contribution
AN - SCOPUS:85129231683
SN - 9789811676178
T3 - Lecture Notes in Networks and Systems
SP - 527
EP - 537
BT - Proceedings of International Conference on Information Technology and Applications.
A2 - Ullah, Abrar
A2 - Gill, Steve
A2 - Rocha, Álvaro
A2 - Anwar, Sajid
PB - Springer
T2 - 15th International Conference on Information Technology and Applications 2021
Y2 - 13 November 2021 through 14 November 2021
ER -