Abstract
Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.
Highlights
Developments in wireless sensing technologies have shown that wireless signals can be deployed to transmit information between wireless communication devices and are able to realize object wireless sensing [1]
Pioneering studies have explored the inherent influence of the human body or human behavior on wireless signal propagation to recognize individuals using commercial WiFi, which is typically referred to as device-free biometrics (DFB)
Motivated by the above insight, in this work, we propose a novel DFB system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features
Summary
Developments in wireless sensing technologies have shown that wireless signals can be deployed to transmit information between wireless communication devices and are able to realize object wireless sensing [1]. Movements of individuals within the coverage of wireless signals will inevitably impact signal propagation. These effects on wireless signals are recorded as channel state information (CSI). The mining and quantifying of such effects in CSI without additional sensors such as cameras, radars, or wearable devices are the main focus of device-free wireless sensing (DFWS). Biometrics or biological recognition is the automatic identification of individuals by quantifying their biological and behavioral characteristics [2]. Pioneering studies have explored the inherent influence of the human body or human behavior on wireless signal propagation to recognize individuals using commercial WiFi, which is typically referred to as device-free biometrics (DFB)
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