Paper Info
Title
Unlocking Smart Phone through Handwaving Biometrics
Abstract
Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. The existing unlocking approaches can be categorized into four groups: motion, password, pattern, and fingerprint. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users' handwaving actions with their smart phones and discover an appealing observation: the waving pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users' waving patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handwaving to verify users. Moreover, we utilize support vector machine (SVM) for accurate and fast classification. Our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware. Results from comprehensive experiments show that the mean false positive rate of OpenSesame is around 15 percent, while the false negative rate is lower than 8 percent.
Year
DOI
Venue
2015
10.1109/TMC.2014.2341633
IEEE Trans. Mob. Comput.
Keywords
Field
DocType
Smart phones,Magnetic sensors,Intelligent sensors,Accelerometers,Mobile computing,Sensor phenomena and characterization
Mobile computing,False positive rate,Authentication,Intelligent sensor,Computer security,Computer science,Usability,Phone,Password,Biometrics
Journal
Volume
Issue
ISSN
14
5
1536-1233
Citations 
PageRank 
References 
12
0.50
18
Authors
7
Name
Order
Citations
PageRank
Lei Yang177848.19
Yi Guo21075.19
Xuan Ding3735.36
Jinsong Han487663.13
Yunhao Liu58810486.66
Cheng Wang628527.81
Changwei Hu7575.95