Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics

Zhao Wang, Chao Shen*, Yufei Chen

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

6 Citations (Scopus)


The increasing usage of smartwatches to access sensitive and personal data while being applied in health monitoring and quick payment, has given rise to the need of convenient and secure authentication technique. However, traditional memory-based authentication methods like PIN are proved to be easily cracked or user-unfriendly. This paper presents a novel approach to unlock smartwatches or authenticate users’ identities on smartwatches by analyzing a users’ handwaving patterns. A filed study was conducted to design typical smartwatch unlocking scenarios and gather users’ handwaving data. Behavioral features were extracted to accurately characterize users’ handwaving patterns. Then a one-class classification algorithm based on scaled Manhattan distance was developed to perform the task of user authentication. Extensive experiments based on a newly established 150-person-time handwaving dataset with a smartwatch, are included to demonstrate the effectiveness of the proposed approach, which achieves an equal-error rate of 4.27% in free-shaking scenario and 14.46% in imitation-attack scenario. This level of accuracy shows that these is indeed identity information in handwaving behavior that can be used as a wearable authentication mechanism.

Original languageEnglish
Title of host publicationBiometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings
EditorsYunhong Wang, Yu Qiao, Jie Zhou, Jianjiang Feng, Zhenan Sun, Zhenhua Guo, Shiguang Shan, Linlin Shen, Shiqi Yu, Yong Xu
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319699226
Publication statusPublished - 2017
Externally publishedYes
Event12th Chinese Conference on Biometric Recognition, CCBR 2017 - Beijing, China
Duration: 28 Oct 201729 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10568 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Chinese Conference on Biometric Recognition, CCBR 2017


  • Motion sensor
  • Smartwatch unlocking
  • User authentication
  • Wearable devices

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