TY - GEN
T1 - Enhancing biometric security
T2 - 12th EAI International Conference on Cloud Computing, CloudComp 2024
AU - Migacz, Lukasz
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2026.
PY - 2025/7/23
Y1 - 2025/7/23
N2 - This study explores the use of Channel State Information for biometric authentication, focusing on addressing the challenges posed by environmental variations. To achieve this, experiments were conducted using off-the-shelf ESP32 devices to collect CSI data across different environments, including urban, suburban, and rural settings. The primary objective was to analyze the influence of external environmental factors on the accuracy of CSI-based biometric systems and to develop methods to mitigate these effects. The significant subcarrier selection method was combined with a weighted Random Forest classifier to improve the system's performance. The results demonstrated that certain subcarriers are more sensitive to environmental changes, and by assigning different weights to these subcarriers the authentication accuracy improved to 93.33%. These findings highlight the potential of CSI-based biometrics to offer reliable and environment-independent authentication, making them suitable for real-world applications in dynamic settings, such as smart homes and vehicular systems. This research lays the groundwork for further studies aimed at developing more resilient biometric systems capable of operating effectively across diverse environments.
AB - This study explores the use of Channel State Information for biometric authentication, focusing on addressing the challenges posed by environmental variations. To achieve this, experiments were conducted using off-the-shelf ESP32 devices to collect CSI data across different environments, including urban, suburban, and rural settings. The primary objective was to analyze the influence of external environmental factors on the accuracy of CSI-based biometric systems and to develop methods to mitigate these effects. The significant subcarrier selection method was combined with a weighted Random Forest classifier to improve the system's performance. The results demonstrated that certain subcarriers are more sensitive to environmental changes, and by assigning different weights to these subcarriers the authentication accuracy improved to 93.33%. These findings highlight the potential of CSI-based biometrics to offer reliable and environment-independent authentication, making them suitable for real-world applications in dynamic settings, such as smart homes and vehicular systems. This research lays the groundwork for further studies aimed at developing more resilient biometric systems capable of operating effectively across diverse environments.
KW - Channel State Information
KW - Environment-Independent
KW - ESP32
KW - Radio Biometrics
KW - Random Forest Classifier
KW - Wi-Fi Sensing
UR - https://www.scopus.com/pages/publications/105012245857
U2 - 10.1007/978-3-031-92517-7_14
DO - 10.1007/978-3-031-92517-7_14
M3 - Conference contribution
AN - SCOPUS:105012245857
SN - 9783031925160
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 183
EP - 200
BT - Cloud Computing - 12th EAI International Conference, CloudComp 2024, Proceedings
A2 - Feng, Xiaohua
A2 - Siarry, Patrick
A2 - Han, Liangxiu
A2 - Yang, Longzhi
PB - Springer
Y2 - 9 September 2024 through 10 September 2024
ER -