On the Applicability of Computer Vision based Gaze Tracking in Mobile Scenarios

Hohlfeld, Oliver; Pomp, André; Link, Jó Ágila Bitsch; Guse, Dennis

doi:10.1145/2785830.2785869

Cited by 19 publications

(12 citation statements)

References 22 publications

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“…Wood and Bulling improved over that in EyeTab where they used a model-based approach for gaze estimation that did not require calibration [49]. Hohlfeld et al then evaluated EyeTab for multiple use cases [23]. Krafka et al and Huang et al introduced gaze estimation datasets for users of mobile devices in controlled settings [27,33].…”

Section: Face and Eyes Detection On Commodity Smartphonesmentioning

confidence: 99%

“…In most of these works, the device was not held naturally by users' but was fixed by using a stand [18,23,24,26,28,33,43,49], by using a headset [37], or by having another person hold it [51]. Even in works where the user was holding the phone, the authors reported that detection failures were often due to users not holding the phone in a way that would show their face and eyes in the camera's view [27,29,30,31,36].…”

Section: Face and Eyes Detection On Commodity Smartphonesmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild

Khamis

Baier

Henze

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Figure 1. In our dataset of photos taken from front-facing cameras of smartphones used in the wild, the face is visible in only 29% of the cases. However, the eyes, and not the whole face, are visible 48% of the time. We derive multiple implications for face and eye detection on mobile devices. For example, our analysis suggests that gaze estimation on mobile devices should rely less on full-face images but estimate gaze based on the eyes only instead.

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Section: Face and Eyes Detection On Commodity Smartphonesmentioning

confidence: 99%

Section: Face and Eyes Detection On Commodity Smartphonesmentioning

confidence: 99%

Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild

Khamis

Baier

Henze

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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show abstract

“…There is a need for a wide range of authentication mechanisms to fit different user preferences, tasks and contexts. Meanwhile, advances in remote gaze estimation enable eye tracking [11,27] and gaze gestures detection [12,13,22,28] using the front-facing cameras of unmodified mobile devices. These advances enable systems to use gaze for mobile authentication [12,13,16,20].…”

Section: Introductionmentioning

confidence: 99%

GazeTouchPIN: protecting sensitive data on mobile devices using secure multimodal authentication

Khamis

Hassib

Zezschwitz

et al. 2017

Proceedings of the 19th ACM International Conference on Multimodal Interaction

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Figure 1: We propose using gaze and touch for authentication on smartphones when accessing sensitive data. GazeTouchPIN is robust against shoulder surfing since it requires attackers to observe the user's eyes and the touchscreen simultaneously. In the example the user enters "6641". Users first select a row of two digits via touch, then gaze left or right to determine the digit to select. To complicate shoulder surfing, one of two digits layouts is randomly chosen at every entry (e.g., compare A to C).

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“…However, we used an emulator on a black background on the Tobii's desktop monitor-based eye tracker (See Figure 1). While eye-tracking systems do currently exist for mobile devices, they still tend to have problems with accuracy (Hohlfeld et al, 2015). Cuadrat et al (2012) listed many advantages of using desktop monitor-based eye tracker over other techniques.…”

Section: Profilementioning

confidence: 99%

Correlating Cross-Platform Usability Problems with Eye Tracking Patterns

Majrashi

Hamilton

Uitdenbogerd

2016

Electronic Workshops in Computing

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Evaluating the cross-platform usability of multiple interactive systems has become increasingly essential. Despite eye tracking being used to supplement traditional usability assessment, there is little research on its use for cross-platform usability evaluation. Our exploratory study seeks relationship between eye-tracking metrics and cross-platform usability problems. We user-tested three cross-platform services and identified a set of usability problems. We separated the identified problems into traditional and cross-platform usability problems. Some of the cross-platform usability problems were associated with users' eye-tracking patterns. We found that consistency on many levels is a major problem cross-platform and we recommend some considerations for evaluators to use as indicators to predict possible cross-platform usability problems.

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On the Applicability of Computer Vision based Gaze Tracking in Mobile Scenarios

Cited by 19 publications

References 22 publications

Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild

Understanding Face and Eye Visibility in Front-Facing Cameras of Smartphones used in the Wild

GazeTouchPIN: protecting sensitive data on mobile devices using secure multimodal authentication

Correlating Cross-Platform Usability Problems with Eye Tracking Patterns

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