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Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

Yu Sun1,2,*, Qiyuan Gao3, Xiaofan Du3, Zhao Gu3

1 College of Business Administration, Shenyang University, Shenyang, 110044, China.
2 College of Business Administration, Northeastern University, Shenyang, 110169, China.
3 Software College, Northeastern University, Shenyang, 110169, China.
* Corresponding Author: Yu Sun. Email:

Computers, Materials & Continua 2019, 61(3), 1365-1375.


In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are introduced to train the collected data on the server end, and the results are finally yielded by the weighted average. The results show that the method this paper proposes has great FRR (False Reject Rate) and FAR (False Accept Rate).


Cite This Article

Y. Sun, Q. Gao, X. Du and Z. Gu, "Smartphone user authentication based on holding position and touch-typing biometrics," Computers, Materials & Continua, vol. 61, no.3, pp. 1365–1375, 2019.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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