Non-Intrusive Biometric Identification for Personalized Computing Using Wireless Big Data

Zhao, Zhiwei; Zhao, Zifei; Min, Geyong; Chen, Shu; Wang, Zhe; Huang, Duan

doi:10.1109/smartworld.2018.00163

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…User identity authentication is an important premise for almost all applications because it can prevent illegal access to private information. Currently, a great deal of applications based on CSI realize user's identity recognition, including WiFi-ID [112], WiWho [113], WifiU [114], FreeSense [115], Wii [116], Chen et al [117], AutoID [118], Wi-Sign [119], Liu et al [120], BodyPIN [121], BioID [122], WiPIN [128], Shi et al [160], and WiAU [161]. Many user authentication systems apply pattern-based methods except WiAU [161], NeuralWave [162], FingerPass [163], Kong et al [163], and WiPIN [128].…”

Section: E Discussion On User Authenticationmentioning

confidence: 99%

“…Most experimental environments contain 2-3 kinds of scenarios, such as the office, laboratory, and apartment, etc. Like crowd counting, most authentication applications utilize walking to determine user identity except BioID [122] and Wi-Sign [119], which utilities lip-motion and hand-motion to identify user's identity, respectively. The best recognition accuracy is Wii [116], which achieves 98.7% for two user's authentications.…”

Section: E Discussion On User Authenticationmentioning

confidence: 99%

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A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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Section: E Discussion On User Authenticationmentioning

confidence: 99%

Section: E Discussion On User Authenticationmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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“…Similarly, Hong et al [62] also applied the sliding window approach to detect the starting and ending points. In the BioID [68], the authors determined the starting and ending points of the lip movements by setting thresholds. In addition, the thresholds were experimentally determined.…”

Section: Signal Segmentationmentioning

confidence: 99%

A Survey of User Authentication Based on Channel State Information

Wang

Dou

et al. 2021

Wireless Communications and Mobile Computing

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Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device-free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state-of-the-art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

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“…Furthermore, in [146], a CSI-based method for biometric identification is presented. The proposed method namely BioID tracks simple lip motions to identify different users.…”

Section: Channel State Information (Csi)mentioning

confidence: 99%

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

Al-qaness

Elaziz

Kim

et al. 2019

Sensors

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Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

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Non-Intrusive Biometric Identification for Personalized Computing Using Wireless Big Data

Cited by 6 publications

References 18 publications

A Survey on Human Behavior Recognition Using Channel State Information

A Survey on Human Behavior Recognition Using Channel State Information

A Survey of User Authentication Based on Channel State Information

Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey

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