GazeTouchPass

Khamis, Mohamed; Alt, Florian; Hassib, Mariam; Zezschwitz, Emanuel von; Hasholzner, Regina; Bulling, Andreas

doi:10.1145/2851581.2892314

Cited by 83 publications

(62 citation statements)

References 30 publications

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“…Accuracy. Applications that rely on explicit eye input usually require high-accuracy gaze estimates, such as for eye typing [31,40], authentication [26], or system control [44]. However, the allowed gaze estimation error depends on the sizes of the gaze targets.…”

Section: Gaze Applicationsmentioning

confidence: 99%

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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100

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Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (www.opengaze.org), the first software toolkit for appearance-based gaze estimation and interaction. CCS CONCEPTS• Computing methodologies → Computer vision.

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Section: Gaze Applicationsmentioning

confidence: 99%

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang¹,

Sugano

Bulling

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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100

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“…• Gaze gestures: By combining gaze and touch, Khamis et al. [68] introduced multimodal authentication for mobile IoT devices, which is more secure than single-modal authentication against against iterative attacks and side attacks. • Electrocardiogram: Electrocardiogram methods can conceal the biometric features during authentication, which are classified as either electrocardiogram with the fiducial features of segmented heartbeats or electrocardiogram with non-fiducial features as discussed in [71] [72].…”

Section: Bio-featuresmentioning

confidence: 99%

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Ferrag

Μαγλαράς

Derhab

2019

Security and Communication Networks

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Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models' development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, We analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, We conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.

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“…The advantage of GTmoPass is that it employs multimodal authentication as a knowledge factor, and personal mobile devices as a possession factor. Multimodal authentication was shown to be highly resilient to shoulder surfing [19]. Furthermore, thermal and smudge attacks normally require the attacker to inspect the interface after the user had left [1,3].…”

Section: Multifactor Authenticationmentioning

confidence: 99%

“…For the knowledge factor in GTmoPass, we employ a modified version of GazeTouchPass [19], a state-of-the art authentication scheme that is highly resilient to shoulder surfing attacks. GazeTouchPass is a multimodal scheme that employs combinations of touch-based PINs (0-9) and eye movements (gaze gestures to the left and to the right).…”

Section: The Knowledge Factormentioning

confidence: 99%

“…Users authenticate by entering a shoulder-surfing resilient GazeTouch password (knowledge factor) on their personal mobile device (possession factor). Users simply need to launch the GazeTouch-Pass app [19] that we modified so that it would securely communicate the password to an authentication server, whose URL is received by the phone through an Eddystone-URL broadcasted using a BLE Beacon.…”

Section: Introductionmentioning

confidence: 99%

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GTmoPass

Khamis

Hasholzner

Bulling

et al. 2017

Proceedings of the 6th ACM International Symposium on Pervasive Displays

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As public displays continue to deliver increasingly private and personalized content, there is a need to ensure that only the legitimate users can access private information in sensitive contexts. While public displays can adopt similar authentication concepts like those used on public terminals (e.g., ATMs), authentication in public is subject to a number of risks. Namely, adversaries can uncover a user's password through (1) shoulder surfing, (2) thermal attacks, or (3) smudge attacks. To address this problem we propose GTmoPass, an authentication architecture that enables Multi-factor user authentication on public displays. The first factor is a knowledge-factor: we employ a shoulder-surfing resilient multimodal scheme that combines gaze and touch input for password entry. The second factor is a possession-factor: users utilize their personal mobile devices, on which they enter the password. Credentials are securely transmitted to a server via Bluetooth beacons. We describe the implementation of GTmoPass and report on an evaluation of its usability and security, which shows that although authentication using GTmoPass is slightly slower than traditional methods, it protects against the three aforementioned threats.

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GazeTouchPass

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 83 publications

References 30 publications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

GTmoPass

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