Abstract
On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 International Conference on Biometrics, ICB 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 255-262 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538642856 |
| DOIs | |
| State | Published - Jul 13 2018 |
| Event | 11th IAPR International Conference on Biometrics, ICB 2018 - Gold Coast, Australia Duration: Feb 20 2018 → Feb 23 2018 |
Other
| Other | 11th IAPR International Conference on Biometrics, ICB 2018 |
|---|---|
| Country | Australia |
| City | Gold Coast |
| Period | 2/20/18 → 2/23/18 |
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Keywords
- Hand Geometry
- In Air Handwriting
- User Authentication
ASJC Scopus subject areas
- Instrumentation
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Pathology and Forensic Medicine
Cite this
Multifactor user authentication with in-air-handwriting and hand geometry. / Lu, Duo; Huang, Dijiang; Deng, Yuli; Alshamrani, Adel.
Proceedings - 2018 International Conference on Biometrics, ICB 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 255-262.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multifactor user authentication with in-air-handwriting and hand geometry
AU - Lu, Duo
AU - Huang, Dijiang
AU - Deng, Yuli
AU - Alshamrani, Adel
PY - 2018/7/13
Y1 - 2018/7/13
N2 - On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.
AB - On wearable and Virtual Reality (VR) platforms, user authentication is a basic function, but usually a keyboard or touchscreen cannot be provided to type a password. Hand gesture and especially in-air-handwriting can be potentially used for user authentication because a gesture input interface is readily available on these platforms. However, determining whether a login request is from the legitimate user based on a piece of hand movement is challenging in both signal processing and matching, which leads to limited performance in existing systems. In this paper, we propose a multifactor user authentication framework using both the motion signal of a piece of in-air-handwriting and the geometry of hand skeleton captured by a depth camera. To demonstrate this framework, we invented a signal matching algorithm, implemented a prototype, and conducted experiments on a dataset of 100 users collected by us. Our system achieves 0.6% Equal Error Rate (EER) without spoofing attack and 3.4% EER with spoofing only data, which is a significant improvement compared to existing systems using the Dynamic Time Warping (DTW) algorithm. In addition, we presented an in-depth analysis of the utilized features to explain the reason for the performance boost.
KW - Hand Geometry
KW - In Air Handwriting
KW - User Authentication
UR - http://www.scopus.com/inward/record.url?scp=85050985163&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050985163&partnerID=8YFLogxK
U2 - 10.1109/ICB2018.2018.00046
DO - 10.1109/ICB2018.2018.00046
M3 - Conference contribution
AN - SCOPUS:85050985163
SP - 255
EP - 262
BT - Proceedings - 2018 International Conference on Biometrics, ICB 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -