Wi-BioScan: Human identification based on radio shadows
01 Oct 2017-pp 1-3
TL;DR: It is observed that the subject can be identified by comprehensive analysis of the received radio shadow and its derivative signatures when a subject is placed in-between the line-of-sight of a closely placed pair of wireless nodes.
Abstract: Subject identification is essential in smart environments. Identification of differently disguised human subjects through visual surveillance methods is extremely challenging. Subject may use various artifacts to defy methods based on body shape and size matching. The radio imaging through wireless sensing is ubiquitous, device-free and privacy-preserving. This paper is based on our observation when a subject is placed in-between the line-of-sight of a closely placed pair of wireless nodes. A radio shadow is observed at the receiver due to interaction of the subject with signals. We observed that the subject can be identified by comprehensive analysis of the received radio shadow and its derivative signatures. Wi-Bioscan is the first ever system to find uniqueness among human radio shadows to identify the subject even if it is disguised in four different ways. During our investigation, we evaluated Wi-BioScan system for different indoor/outdoor locations. System is ubiquitous and scalable due to its feature of location independence and generation of a rich template. The method is also robust against surrounding dynamics such as other human presence and object movements.
References
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22 Jan 2011
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.
1,354 citations
"Wi-BioScan: Human identification ba..." refers methods in this paper
...We have collected CSI samples [11] at 100Hz packet rate with each recording of size 5 seconds for 20 subjects standing in middle of the LOS between the nodes....
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17 Aug 2015
TL;DR: SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems.
Abstract: This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.
1,159 citations
Additional excerpts
...As the propagation paths vary spatially due to interference with subject, the angle-of-arrival (AOA) estimated using [10] at receiver also changes uniquely with the subject....
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Book•
31 May 2000
TL;DR: The issues addressed in this book are highly relevant to many fundamental concerns of both researchers and practitioners of automated biometrics in computer and system security.
Abstract: Biometrics-based authentication and identification are emerging as the most reliable method to authenticate and identify individuals. Biometrics requires that the person to be identified be physically present at the point-of-identification and relies on `something which you are or you do' to provide better security, increased efficiency, and improved accuracy. Automated biometrics deals with physiological or behavioral characteristics such as fingerprints, signature, palmprint, iris, hand, voice and face that can be used to authenticate a person's identity or establish an identity from a database. With rapid progress in electronic and Internet commerce, there is also a growing need to authenticate the identity of a person for secure transaction processing. Designing an automated biometrics system to handle large population identification, accuracy and reliability of authentication are challenging tasks. Currently, there are over ten different biometrics systems that are either widely used or under development. Some automated biometrics, such as fingerprint identification and speaker verification, have received considerable attention over the past 25 years, and some issues like face recognition and iris-based authentication have been studied extensively resulting in successful development of biometrics systems in commercial applications. However, very few books are exclusively devoted to such issues of automated biometrics. Automated Biometrics: Technologies and Systems systematically introduces the technologies and systems, and explores how to design the corresponding systems with in-depth discussion. The issues addressed in this book are highly relevant to many fundamental concerns of both researchers and practitioners of automated biometrics in computer and system security.
395 citations
26 May 2016
TL;DR: For the first time WiFi signals can also be used to uniquely identify people and a system called WiFi-ID is proposed that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow for uniquely identify that person.
Abstract: Prior research has shown the potential of device-free WiFi sensing for human activity recognition. In this paper, we show for the first time WiFi signals can also be used to uniquely identify people. There is strong evidence that suggests that all humans have a unique gait. An individual's gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. We implement WiFi-ID on commercial off-the-shelf devices. We conduct extensive experiments to demonstrate that our system can uniquely identify people with average accuracy of 93% to 77% from a group of 2 to 6 people, respectively. We envisage that this technology can find many applications in small office or smart home settings.
214 citations
"Wi-BioScan: Human identification ba..." refers methods in this paper
...Behavioral biometric features like human gait have been extracted using wireless sensing [4] but have limited accuracy for a large number of subjects....
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04 Jun 2013
TL;DR: A framework, termed as Aravrta1, is proposed, which classifies the local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (Regions with disguise) classes, and improves the performance compared to existing algorithms.
Abstract: Face verification, though for humans seems to be an easy task, is a long-standing research area. With challenging covariates such as disguise or face obfuscation, automatically verifying the identity of a person is assumed to be very hard. This paper explores the feasibility of face verification under disguise variations using multi-spectrum (visible and thermal) face images. We propose a framework, termed as Aravrta1, which classifies the local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (regions with disguise) classes. The biometric patches are then used for facial feature extraction and matching. The performance of the algorithm is evaluated on the IHTD In and Beyond Visible Spectrum Disguise database that is prepared by the authors and contains images pertaining to 75 subjects with different kinds of disguise variations. The experimental results suggest that the proposed framework improves the performance compared to existing algorithms, however there is a need for more research to address this important covariate.
108 citations
"Wi-BioScan: Human identification ba..." refers methods in this paper
...Methods utilize image processing to extract features from disguised human faces to match them from actual faces [7]....
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