Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

This study introduces a novel authentication methodology; it is based on pattern recognition of fingers size and pressure when users touch smartphone screen. By analyzing diagrams of these touches and applying data mining for the first time as an authentication technique, this paper presents three new approaches. First, an exact-range evaluation approach has been verified that size is more recognition consistency than pressure. Second, a pattern-range is a new technique reliance on size frequency position. At last, using a size-range has been facilitated the login. The association rules have been modified to work on finger touchscreen data files. To login, 94.1111% of 18 authorized users are succeeded and 98.9% of 20 unauthorized users are failed. Android device and Android studio are used. Size and pressure are normalized to 1; a training set is applied; the password is not considered.

[1]  Dong Ryeol Shin,et al.  Force-touch measurement methodology based on user experience , 2018, Int. J. Distributed Sens. Networks.

[2]  Chen Liang,et al.  HandSee: Enabling Full Hand Interaction on Smartphone with Front Camera-based Stereo Vision , 2019, CHI.

[3]  Dhamea A. Jasm,et al.  Deep image mining for convolution neural network , 2020 .

[4]  Jun Ho Huh,et al.  Gesture Authentication for Smartphones: Evaluation of Gesture Password Selection Policies , 2020, 2020 IEEE Symposium on Security and Privacy (SP).

[5]  Kamal Z Zamli,et al.  Press touch code: A finger press based screen size independent authentication scheme for smart devices , 2017, PloS one.

[6]  Adam J. Aviv,et al.  This PIN Can Be Easily Guessed: Analyzing the Security of Smartphone Unlock PINs , 2020, 2020 IEEE Symposium on Security and Privacy (SP).

[7]  Paul Strohmeier,et al.  Optimizing Pressure Matrices: Interdigitation and Interpolation Methods for Continuous Position Input , 2019, Tangible and Embedded Interaction.

[8]  Jonna Häkkilä,et al.  Exploring finger specific touch screen interaction for mobile phone user interfaces , 2014, OZCHI.

[9]  Nasir D. Memon,et al.  Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices , 2018, Comput. Secur..

[10]  D. J. Farlie,et al.  On Thinking Statistically , 1964 .

[11]  Shuo Gao,et al.  High Three-Dimensional Detection Accuracy in Piezoelectric-Based Touch Panel in Interactive Displays by Optimized Artificial Neural Networks , 2019, Sensors.

[12]  Zhi-Li Zhang,et al.  Multi-touch Authentication Using Hand Geometry and Behavioral Information , 2017, 2017 IEEE Symposium on Security and Privacy (SP).

[13]  Amir Hossein Alavi,et al.  An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring , 2019, Future Gener. Comput. Syst..

[14]  Pieter Coenen,et al.  The associations of mobile touch screen device use with musculoskeletal symptoms and exposures: A systematic review , 2017, PloS one.

[15]  Dimitrios Iakovakis,et al.  Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease , 2018, Scientific reports.

[16]  Samir Abou El-Seoud,et al.  A Novel Model for Securing Mobile-based Systems against DDoS Attacks in Cloud Computing Environment , 2019, Int. J. Interact. Mob. Technol..

[17]  Haiqing Liu,et al.  Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices , 2020, Int. J. Interact. Mob. Technol..

[18]  Niels Henze,et al.  InfiniTouch: Finger-Aware Interaction on Fully Touch Sensitive Smartphones , 2018, UIST.

[19]  Niels Henze,et al.  Estimating the Finger Orientation on Capacitive Touchscreens Using Convolutional Neural Networks , 2017, ISS.