User-Authentication on Wearable Devices Based on Punch Gesture Biometrics
Due to commoditization and convenience, wearable technology are interwoven with our daily life. However, privacy sensitive data stored on those devices such as personal email, message can be easily stolen. Most devices require a PIN input to unlock. However, this mechanism is vulnerable to shoulder...
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2017-01-01
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Online Access: | https://doi.org/10.1051/itmconf/20171101003 |
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doaj-33e5c766ed9842118996cd09cfe845732021-02-02T04:05:49ZengEDP SciencesITM Web of Conferences2271-20972017-01-01110100310.1051/itmconf/20171101003itmconf_ist2017_01003User-Authentication on Wearable Devices Based on Punch Gesture BiometricsLiang Guan-ChengXu Xiang-YuYu Jia-DiDue to commoditization and convenience, wearable technology are interwoven with our daily life. However, privacy sensitive data stored on those devices such as personal email, message can be easily stolen. Most devices require a PIN input to unlock. However, this mechanism is vulnerable to shoulder surfing attack. Thus many novel authentication approaches have been proposed to solve this problem. And biometric-based methods have been adopted by many researchers because of the efficiency and excellent performance. In this paper, we propose a new biometric-based authentication system. We focus on how the user performs a straight punch gesture subconsciously. By analysis the acceleration data from the smartwatch when user performing the gesture, we are able to profile the user. And we authenticate the user according to the biometrics of this action. This mechanism is light-weighted and do not require user to remember any secret code. We develop an authentication system on Samsung Gear Fit 2 and conducted a real-world experiment on 20 volunteers. And we collected 13000 gesture samples to evaluate our system. Results show that our system can achieve a classification accuracy of at least 95.45%. In attacking scenario, our system can achieve an equal error rate lower than 4%. The maximum number of samples required by a well-trained classifier is 25.https://doi.org/10.1051/itmconf/20171101003 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liang Guan-Cheng Xu Xiang-Yu Yu Jia-Di |
spellingShingle |
Liang Guan-Cheng Xu Xiang-Yu Yu Jia-Di User-Authentication on Wearable Devices Based on Punch Gesture Biometrics ITM Web of Conferences |
author_facet |
Liang Guan-Cheng Xu Xiang-Yu Yu Jia-Di |
author_sort |
Liang Guan-Cheng |
title |
User-Authentication on Wearable Devices Based on Punch Gesture Biometrics |
title_short |
User-Authentication on Wearable Devices Based on Punch Gesture Biometrics |
title_full |
User-Authentication on Wearable Devices Based on Punch Gesture Biometrics |
title_fullStr |
User-Authentication on Wearable Devices Based on Punch Gesture Biometrics |
title_full_unstemmed |
User-Authentication on Wearable Devices Based on Punch Gesture Biometrics |
title_sort |
user-authentication on wearable devices based on punch gesture biometrics |
publisher |
EDP Sciences |
series |
ITM Web of Conferences |
issn |
2271-2097 |
publishDate |
2017-01-01 |
description |
Due to commoditization and convenience, wearable technology are interwoven with our daily life. However, privacy sensitive data stored on those devices such as personal email, message can be easily stolen. Most devices require a PIN input to unlock. However, this mechanism is vulnerable to shoulder surfing attack. Thus many novel authentication approaches have been proposed to solve this problem. And biometric-based methods have been adopted by many researchers because of the efficiency and excellent performance. In this paper, we propose a new biometric-based authentication system. We focus on how the user performs a straight punch gesture subconsciously. By analysis the acceleration data from the smartwatch when user performing the gesture, we are able to profile the user. And we authenticate the user according to the biometrics of this action. This mechanism is light-weighted and do not require user to remember any secret code. We develop an authentication system on Samsung Gear Fit 2 and conducted a real-world experiment on 20 volunteers. And we collected 13000 gesture samples to evaluate our system. Results show that our system can achieve a classification accuracy of at least 95.45%. In attacking scenario, our system can achieve an equal error rate lower than 4%. The maximum number of samples required by a well-trained classifier is 25. |
url |
https://doi.org/10.1051/itmconf/20171101003 |
work_keys_str_mv |
AT liangguancheng userauthenticationonwearabledevicesbasedonpunchgesturebiometrics AT xuxiangyu userauthenticationonwearabledevicesbasedonpunchgesturebiometrics AT yujiadi userauthenticationonwearabledevicesbasedonpunchgesturebiometrics |
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