IWink: Exploring Eyelid Gestures on Mobile Devices

Zhen Li; Mingming Fan; Ying Han; Khai N. Truong

IWink: Exploring Eyelid Gestures on Mobile Devices

Zhen Li, Mingming Fan, Ying Han, Khai N. Truong

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

Abstract

Although gaze has been widely studied for mobile interactions, eyelid-based gestures are relatively understudied and limited to few basic gestures (e.g., blink). In this work, we propose a gesture grammar to construct both basic and compound eyelid gestures. We present an algorithm to detect nine eyelid gestures in real-time on mobile devices and evaluate its performance with 12 participants. Results show that our algorithm is able to recognize nine eyelid gestures with 83% and 78% average accuracy using user-dependent and user-independent models respectively. Further, we design a gesture mapping scheme to allow for navigating between and within mobile apps only using eyelid gestures. Moreover, we show how eyelid gestures can be used to enable cross-application and sensitive interactions. Finally, we highlight future research directions.

Original language	English
Title of host publication	HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis
Publisher	Association for Computing Machinery, Inc
Pages	83-89
Number of pages	7
ISBN (Electronic)	9781450381512
Publication status	Published - 12 Oct 2020
Externally published	Yes
Event	1st International Workshop on Human-Centric Multimedia Analysis, HuMA 2020 - Virtual, Online, United States Duration: 12 Oct 2020 → …

Publication series

Name	HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis

Conference

Conference	1st International Workshop on Human-Centric Multimedia Analysis, HuMA 2020
Country/Territory	United States
City	Virtual, Online
Period	12/10/20 → …

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

eyelid gestures
hands-free interaction
mobile interaction

Access to Document

https://hdl.handle.net/1783.1/121714

Cite this

Li, Z., Fan, M., Han, Y., & Truong, K. N. (2020). IWink: Exploring Eyelid Gestures on Mobile Devices. In HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis (pp. 83-89). (HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis). Association for Computing Machinery, Inc. https://hdl.handle.net/1783.1/121714

@inproceedings{d2a3e983276f4f9c921b71aec99d88cc,

title = "IWink: Exploring Eyelid Gestures on Mobile Devices",

abstract = "Although gaze has been widely studied for mobile interactions, eyelid-based gestures are relatively understudied and limited to few basic gestures (e.g., blink). In this work, we propose a gesture grammar to construct both basic and compound eyelid gestures. We present an algorithm to detect nine eyelid gestures in real-time on mobile devices and evaluate its performance with 12 participants. Results show that our algorithm is able to recognize nine eyelid gestures with 83\% and 78\% average accuracy using user-dependent and user-independent models respectively. Further, we design a gesture mapping scheme to allow for navigating between and within mobile apps only using eyelid gestures. Moreover, we show how eyelid gestures can be used to enable cross-application and sensitive interactions. Finally, we highlight future research directions.",

keywords = "eyelid gestures, hands-free interaction, mobile interaction",

author = "Zhen Li and Mingming Fan and Ying Han and Truong, \{Khai N.\}",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 1st International Workshop on Human-Centric Multimedia Analysis, HuMA 2020 ; Conference date: 12-10-2020",

year = "2020",

month = oct,

day = "12",

language = "English",

series = "HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis",

publisher = "Association for Computing Machinery, Inc",

pages = "83--89",

booktitle = "HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis",

}

Li, Z, Fan, M, Han, Y & Truong, KN 2020, IWink: Exploring Eyelid Gestures on Mobile Devices. in HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis. HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis, Association for Computing Machinery, Inc, pp. 83-89, 1st International Workshop on Human-Centric Multimedia Analysis, HuMA 2020, Virtual, Online, United States, 12/10/20. <https://hdl.handle.net/1783.1/121714>

IWink: Exploring Eyelid Gestures on Mobile Devices. / Li, Zhen; Fan, Mingming; Han, Ying et al.
HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis. Association for Computing Machinery, Inc, 2020. p. 83-89 (HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - IWink

T2 - 1st International Workshop on Human-Centric Multimedia Analysis, HuMA 2020

AU - Li, Zhen

AU - Fan, Mingming

AU - Han, Ying

AU - Truong, Khai N.

PY - 2020/10/12

Y1 - 2020/10/12

N2 - Although gaze has been widely studied for mobile interactions, eyelid-based gestures are relatively understudied and limited to few basic gestures (e.g., blink). In this work, we propose a gesture grammar to construct both basic and compound eyelid gestures. We present an algorithm to detect nine eyelid gestures in real-time on mobile devices and evaluate its performance with 12 participants. Results show that our algorithm is able to recognize nine eyelid gestures with 83% and 78% average accuracy using user-dependent and user-independent models respectively. Further, we design a gesture mapping scheme to allow for navigating between and within mobile apps only using eyelid gestures. Moreover, we show how eyelid gestures can be used to enable cross-application and sensitive interactions. Finally, we highlight future research directions.

AB - Although gaze has been widely studied for mobile interactions, eyelid-based gestures are relatively understudied and limited to few basic gestures (e.g., blink). In this work, we propose a gesture grammar to construct both basic and compound eyelid gestures. We present an algorithm to detect nine eyelid gestures in real-time on mobile devices and evaluate its performance with 12 participants. Results show that our algorithm is able to recognize nine eyelid gestures with 83% and 78% average accuracy using user-dependent and user-independent models respectively. Further, we design a gesture mapping scheme to allow for navigating between and within mobile apps only using eyelid gestures. Moreover, we show how eyelid gestures can be used to enable cross-application and sensitive interactions. Finally, we highlight future research directions.

KW - eyelid gestures

KW - hands-free interaction

KW - mobile interaction

UR - https://www.scopus.com/pages/publications/85095605817

M3 - Conference Paper published in a book

AN - SCOPUS:85095605817

T3 - HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis

SP - 83

EP - 89

BT - HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis

PB - Association for Computing Machinery, Inc

Y2 - 12 October 2020

ER -

IWink: Exploring Eyelid Gestures on Mobile Devices

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Bibliographical note

Keywords

Access to Document

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