Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics

Zhao Wang; Chao Shen; Yufei Chen

doi:10.1007/978-3-319-69923-3_59

Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics

Zhao Wang, Chao Shen, Yufei Chen

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

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. © 2017, Springer International Publishing AG.

Original language	English
Title of host publication	Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings
Publisher	Springer Verlag
Pages	545-553
Volume	10568 LNCS
ISBN (Print)	9783319699226
DOIs	https://doi.org/10.1007/978-3-319-69923-3_59
Publication status	Published - 2017
Externally published	Yes
Event	12th Chinese Conference on Biometric Recognition, CCBR 2017 - Beijing, China Duration: 28 Oct 2017 → 29 Oct 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10568 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Chinese Conference on Biometric Recognition, CCBR 2017
Place	China
City	Beijing
Period	28/10/17 → 29/10/17

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61403301 and Grant 61773310, in part by the China Postdoctoral Science Foundation under Grant 2014M560783 and Grant 2015T81032, in part by the Natural Science Foundation of Shaanxi Province under Grant 2015JQ6216, and in part by the Fundamental Research Funds for the Central Universities under Grant xjj2015115.

Research Keywords

Motion sensor
Smartwatch unlocking
User authentication
Wearable devices

Access Output

10.1007/978-3-319-69923-3_59

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Cite this

Wang, Z., Shen, C., & Chen, Y. (2017). Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics. In Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings (Vol. 10568 LNCS, pp. 545-553). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10568 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-69923-3_59

Wang, Zhao ; Shen, Chao ; Chen, Yufei. / Handwaving Authentication : Unlocking Your Smartwatch Through Handwaving Biometrics. Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings. Vol. 10568 LNCS Springer Verlag, 2017. pp. 545-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics",

abstract = "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{\textquoteright} identities on smartwatches by analyzing a users{\textquoteright} handwaving patterns. A filed study was conducted to design typical smartwatch unlocking scenarios and gather users{\textquoteright} handwaving data. Behavioral features were extracted to accurately characterize users{\textquoteright} 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. {\textcopyright} 2017, Springer International Publishing AG.",

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author = "Zhao Wang and Chao Shen and Yufei Chen",

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Wang, Z, Shen, C & Chen, Y 2017, Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics. in Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings. vol. 10568 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10568 LNCS, Springer Verlag, pp. 545-553, 12th Chinese Conference on Biometric Recognition, CCBR 2017, Beijing, China, 28/10/17. https://doi.org/10.1007/978-3-319-69923-3_59

Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics. / Wang, Zhao; Shen, Chao; Chen, Yufei.
Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings. Vol. 10568 LNCS Springer Verlag, 2017. p. 545-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10568 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Handwaving Authentication

T2 - 12th Chinese Conference on Biometric Recognition, CCBR 2017

AU - Wang, Zhao

AU - Shen, Chao

AU - Chen, Yufei

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017

Y1 - 2017

N2 - 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. © 2017, Springer International Publishing AG.

AB - 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. © 2017, Springer International Publishing AG.

KW - Motion sensor

KW - Smartwatch unlocking

KW - User authentication

KW - Wearable devices

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U2 - 10.1007/978-3-319-69923-3_59

DO - 10.1007/978-3-319-69923-3_59

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783319699226

VL - 10568 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 545

EP - 553

BT - Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings

PB - Springer Verlag

Y2 - 28 October 2017 through 29 October 2017

ER -

Wang Z, Shen C, Chen Y. Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics. In Biometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings. Vol. 10568 LNCS. Springer Verlag. 2017. p. 545-553. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-69923-3_59

Handwaving Authentication: Unlocking Your Smartwatch Through Handwaving Biometrics

Abstract

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Research Keywords

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