Continuous User Verification via Respiratory Biometrics

Liu, Jian; Chen, Yingying; Dong, Yudi; Wang, Yan; Zhao, Tianming; Yao, Yu-Dong

doi:10.1109/infocom41043.2020.9155258

Cited by 38 publications

(23 citation statements)

References 32 publications

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“…In addition, different applications may require different actions and feature extraction. For example, reference [89] leverages [90]. Based on the user gaits, it fulfills a challenging and promising task that the system can judge whether a person is a target given the WiFi signal measured and the corresponding user walking in an unknown field and a video segmentation of a walking user in another field area.…”

Section: Robustness Of User Authenticationmentioning

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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Section: Robustness Of User Authenticationmentioning

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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“…Several studies in parallel seek to achieve non-intrusive continuous identity verification using off-the-shelf WiFi devices [77][78][79][80][81] (Figure 13). These methods leverage the fact that the majority of IEEE 802.11 WiFi protocols have the access point (AP) explicitly sending out known pilot symbols, shown in Figure 13(b), prior to the data communication, which allows the receiver(s) to measure the wireless channel effect in terms of signal attenuation, e.g., receive signal strength (RSS), plus phase offset, e.g., channel state information (CSI), and cancel the channel effect for better reception.…”

Section: Wifi-id: Non-contact Human Identification Using Wifi Signalsmentioning

confidence: 99%

“…Similarly, in [78], Liu et al extracted fine-grained CSI of individual OFDM subcarriers from off- In [79], Abdelnasser et al proposed a non-invasive RSS based WiFi breathing estimator (Figure 14). The design leverages the fact that inhalation and exhalation can cause a perceivable periodic pattern in the RSS observed by a device positioned on the chest surface [77].…”

Section: Wifi-id: Non-contact Human Identification Using Wifi Signalsmentioning

confidence: 99%

“…Similarly, in [78], Liu et al extracted fine-grained CSI of individual OFDM subcarriers from off-the-shelf WiFi devices to detect minute movements and provide accurate breathing (inhalation and exhalation) and heartbeat (diastole and systole) estimation, concurrently. The system, shown in Figure 14, utilizes multiple CSIs captured at different OFDM subcarriers to differentiate the small body movement of breathing and heartbeat.…”

Section: Wifi-id: Non-contact Human Identification Using Wifi Signalsmentioning

confidence: 99%

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Radar-Based Non-Contact Continuous Identity Authentication

et al. 2020

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Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.

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“…To preserve the scale of the CSI measurements, CSI conjugate multiplication of two CSI streams from adjacent receiver antennas have been used in different WiFi sensing applications to remove the phase offset, however, it still left with the amplitude impulse noise in the CSI measurements and therefore have to be denoised in the CSI pre-processing stage [353,370]. The performance of CSI-based vital sign detection systems has improved over the years and results in many applications such as stationary person detection [371], derivation of the respiration biometrics for continuous user verification/authentication [372,373], in-vehicle breathing rate monitoring [374,375,309], mobile WiFi sensing using wearables / smartphones [376,377], and indoor healthcare monitoring [378] such as abnormal respiration events detection [379,380,381,291] and contactless sleep monitoring [382,292,359,383,384].…”

Section: Related Workmentioning

confidence: 99%

Tensor computing for big data analytic

Ong¹

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First and foremost, I would like to thank my supervisor, Assoc. Prof. Ng Wee Keong, for his kindness, trust, and patience throughout the years of my PhD study. The research guidance given by Prof. Ng has important influence to the success of our new discovery. The research environment in NTU SCSE provides us the research freedom to carry out meaningful explorations and experimentations with new inventions or innovations. The regular seminars held by the school and university inspires us with new ideas and expand our core research to stay up-todate and relevant. I would like to thank Assoc. Prof. Adams Kong Wai Kin, Assoc.

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Continuous User Verification via Respiratory Biometrics

Cited by 38 publications

References 32 publications

A Survey of User Authentication Based on Channel State Information

A Survey of User Authentication Based on Channel State Information

Radar-Based Non-Contact Continuous Identity Authentication

Tensor computing for big data analytic

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