SkullConduct

Schneegaß, Stefan; Oualil, Youssef; Bulling, Andreas

doi:10.1145/2858036.2858152

Cited by 73 publications

(13 citation statements)

References 18 publications

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“…(3) The paper had to propose an authentication scheme. Specifically, the paper had to use machine learning to label [12], [33], [41], [42] IMWUT/UbiComp [11], [20], [25], [35], [48] INFOCOM [8], [36], [45], [51], [53] MobiCom [15], [32] MobiSys [7], [31] NDSS [5], [17], [49], [50] Pattern Recognition [2], [9], [18], [24], [38], [40], [54]…”

Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

“…EER % ACC % FPR % 0.00 [9] 99.30 [55] 0.00 [11] 0.34 [24] 98.61 [32] 0.01 [9] 0.59 [2] 98.47 [51] 0.10 [35] 0.95 [40] 98.00 [53] 0.10 [5] 1.26 [55] 97.00 [42] 0.10 [40] TABLE III: The top five authentication systems according to a naïve comparison of their best reported values for EER, ACC, and FPR metrics. These metrics are reported the most often but rarely yield a meaningful comparison.…”

Section: B a Naïve Comparisonmentioning

confidence: 99%

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Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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Research has produced many types of authentication systems that use machine learning. However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate. In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed. These common metrics hide the details of the inherent tradeoffs a system must make when implemented. Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed. We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard. We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided. The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way. When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics. Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems.

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Section: Review Of Recent Authentication Systemsmentioning

confidence: 99%

Section: B a Naïve Comparisonmentioning

confidence: 99%

Robust Performance Metrics for Authentication Systems

Sugrim

Liu

McLean³

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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“…A typical WBAN, shown in Figure 3, is an interconnection of multiple independent wearable sensors, each of which measure specific signals from a modality included but not limited to be one of the following: brainwaves associated with stimuli, iris structure [30], retinal patterns [194], vocal resonance while speaking prerecorded phrases [149,195], skull conduction in response to audio waves propagating within the head [184], signals from the heart [23,180], vein pattern on the underside of the skin, gait while walking or striding [64,81], signals generated by muscles in motion [ [201], Mechanomyograph (MMG) signals generated by muscles upon activation [25], fingerprint patterns [57], readings of pressure applied by fingertips when holding objects like pens, car keys, steering wheel, computer mouse, door handle, etc., patterns from signature and body odor [135], and Photoplethysmograph (PPG) denoting the absorption of light through a body part in accordance with heartrate pulses [53,123].…”

Section: Wireless Body Area Network (Wban)mentioning

confidence: 99%

“…Skull Conduction: An integrated bone conduction speaker was designed and proposed at the CHI Conference on Human Factors in Computing Systems in May, 2016, where the authors introduced an embedded wearable, SkullConduct, integrated with wearable computers like Google Glass[184] …”

mentioning

confidence: 99%

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

2019

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Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design. A. Sundararajan et al. • TBS consider each modality of the user as a separate entity while WBS consider the entire user (along with all of their individual modalities) as one [152] • TBS can be optionally connected to the Internet, while WBS are inherently online, exploiting the principles of Internet of Everything (IoE) [178] • The authentication and identification processes for TBS are static and user-initiated while for WBS are dynamic and autonomous

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“…CHI 2017, May 6-11, 2017, Denver, CO, USA superficial vein structure, fingerprint, ear shape, hand geometry, retinal pattern, palm print, bone conduction through the skull, voice, written signature, and DNA [27,19,5,47,24,20,40]. Yampolskiy and Govindaraju describe behavioral biometrics in a comprehensive survey [50], whereas Cornelius and Gutierrez focus on mobile contexts in their survey [11].…”

Section: Environmental Sensingmentioning

confidence: 99%

Zensei

Sato

Puri

Olwal

et al. 2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Figure 1. Zensei embeds implicit and uninterrupted user identification in mobile devices, furniture or the environment. Our custom, wide-spectrum multi-electrode sensing hardware allows high-speed wireless data collection of the electrical characteristics of users. A longitudinal 22-day experiment with 46 subjects experiment shows promising classification accuracy and low false acceptance rate. The miniaturized wireless Zensei sensor board (right) has a microprocessor, power management circuit, analog sensing circuit, and Bluetooth module, and is powered by a lithium polymer battery.

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SkullConduct

Cited by 73 publications

References 18 publications

Robust Performance Metrics for Authentication Systems

Robust Performance Metrics for Authentication Systems

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

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