TY - GEN
T1 - Global Feature Analysis and Comparative Evaluation of Freestyle In-Air-Handwriting Passcode for User Authentication
AU - Lu, Duo
AU - Deng, Yuli
AU - Huang, Dijiang
N1 - Funding Information:
∗The author Duo Lu is also affiliated with Arizona State University, duolu@asu.edu, and this work is done when Duo Lu is a PhD student at Arizona State University. †This research is sponsored by NSF grant CNS-1925709 Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. ACSAC ’21, December 6–10, 2021, Virtual Event, USA © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8579-4/21/12...$15.00 https://doi.org/10.1145/3485832.3485906
Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/12/6
Y1 - 2021/12/6
N2 - Freestyle in-air-handwriting passcode-based user authentication methods address the needs for Virtual Reality (VR)/Augmented Reality (AR) headsets, wearable devices, and game consoles where a physical keyboard cannot be provided for typing a password, but a gesture input interface is readily available. Such an authentication system can capture the hand movement of writing a passcode string in the air and verify the user identity using both the writing content (like a password) and the writing style (like a behavior biometric trait). However, distinguishing handwriting signals from different users is challenging in signal processing, feature extraction, and matching. In this paper, we provide a detailed analysis of the global features of in-air-handwriting signals and a comparative evaluation of such a user authentication framework. Also, we build a prototype system with two different types of hand motion capture devices, collect two datasets, and conduct an extensive evaluation.
AB - Freestyle in-air-handwriting passcode-based user authentication methods address the needs for Virtual Reality (VR)/Augmented Reality (AR) headsets, wearable devices, and game consoles where a physical keyboard cannot be provided for typing a password, but a gesture input interface is readily available. Such an authentication system can capture the hand movement of writing a passcode string in the air and verify the user identity using both the writing content (like a password) and the writing style (like a behavior biometric trait). However, distinguishing handwriting signals from different users is challenging in signal processing, feature extraction, and matching. In this paper, we provide a detailed analysis of the global features of in-air-handwriting signals and a comparative evaluation of such a user authentication framework. Also, we build a prototype system with two different types of hand motion capture devices, collect two datasets, and conduct an extensive evaluation.
KW - Gesture input interface
KW - In-air-handwriting
KW - User authentication
UR - http://www.scopus.com/inward/record.url?scp=85121636746&partnerID=8YFLogxK
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U2 - 10.1145/3485832.3485906
DO - 10.1145/3485832.3485906
M3 - Conference contribution
AN - SCOPUS:85121636746
T3 - ACM International Conference Proceeding Series
SP - 468
EP - 481
BT - Proceedings - 37th Annual Computer Security Applications Conference, ACSAC 2021
PB - Association for Computing Machinery
T2 - 37th Annual Computer Security Applications Conference, ACSAC 2021
Y2 - 6 December 2021 through 10 December 2021
ER -