NDLI: Modeling free-form handwriting gesture user authentication for Android smartphones

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Modeling free-form handwriting gesture user authentication for Android smartphones

Content Provider	ACM Digital Library
Author	Vea, Larry A. Espinosa, Floren Alexis T. Guerrero III, Guillermo Gohan E.
Abstract	Smartphones nowadays are customized to help users with their daily tasks such as storing important data or making transactions through the internet. With the sensitivity of the data involved, authentication mechanism such as fixed-text password, PIN, or unlock patterns are used to safeguard these data against intruders. However, these mechanisms have the risk from security threats such as cracking or shoulder surfing. To enhance mobile and/or information security, this study aimed to develop a free-form handwriting gesture user authentication for smartphones. It also tried to discover the static and dynamic handwriting features that significantly influence the recognition of a legitimate user. The experiment was then conducted by asking thirty (30) individuals to draw or swipe using their fingertip their desired free-form security pattern ten (10) times. These patterns were then cleaned and processed, and extracted seven (7) static and eleven (11) dynamic handwriting features. By means of Neural Network classifier of the RapidMiner data mining tool, these features were used to develop, validate, and test a model for user authentication. The model showed a very promising recognition rate of 96.67%. The model is further tested through a prototype, and it still gave a very satisfactory result.
Starting Page	3
Ending Page	6
Page Count	4
File Format	PDF
ISBN	9781450341783
DOI	10.1145/2897073.2897095
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Authentication Information security Gesture Model Shoulder surfing
Content Type	Text
Resource Type	Article

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