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A Study on User Authentication with Smartphone Accelerometer Sensor
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 Title & Authors
A Study on User Authentication with Smartphone Accelerometer Sensor
Seo, Jun-seok; Moon, Jong-sub;
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 Abstract
With the growth of financial industry with smartphone, interest on user authentication using smartphone has been arisen in these days. There are various type of biometric user authentication techniques, but gait recognition using accelerometer sensor in smartphone does not seem to develop remarkably. This paper suggests the method of user authentication using accelerometer sensor embedded in smartphone. Specifically, calibrate the sensor data from smartphone with 3D-transformation, extract features from transformed data and do principle component analysis, and learn model with using gaussian mixture model. Next, authenticate user data with confidence interval of GMM model. As result, proposed method is capable of user authentication with accelerometer sensor on smartphone as a high degree of accuracy(about 96%) even in the situation that environment control and limitation are minimum on the research.
 Keywords
Biometric;gait recognition;accelerometer sensor;pattern recognition;machine learning;authentication;
 Language
Korean
 Cited by
 References
1.
Nikolaos V. Boulgouris, Dimitrios Hatzinakos, and Konstantinos N. Plataniotis, "Gait Recognition: A challenging signal processing technology for biometric identification" IEEE signal processing magazine. 2005

2.
Jeon Myung Joong, Park Young Tack, "Robust User Activity Recognition using Smartphone Accelerometer Sensors," Korea Information Processing Society/Software and Data Science, Sep. 2013

3.
Junho Ahn, Rechard Han, "Personalized behavior pattern recognition and unusual event detection for mobile users," Mobile Information Systems 9(2013) 99-122 crossref(new window)

4.
Vijay Bhaskar Semwal, Manish Raj, G.C. Nandi, "Biometric gait identification based on a multilayer perceptron," Robotics and Autonomous Systems 65(2015)65-75 crossref(new window)

5.
Claudia Nickel, Tobias Wirtl, "Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm, 10(2), pp. 100-103, Feb. 2012

6.
Heikki Ailisto, Jani Mantyjarvi, Elena Vildjiounaite and Satu-Marja Makela, "Identifying People from Gait Pattern with Accelerometers," The International society for optical engineering, 2005

7.
Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore, "Cell Phone-Based Biometric Identification," Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on Biometrics Compendium

8.
Claudia Nickel, Tobias Wirtl, and Christoph Busch, "Authentication of Smartphone Users Based on the Way They Walk Using k-NN Algorithm," Conference on Intelligent information Hiding and Multimedia Signal Processing, 2012

9.
Ahmet Turan Ozdemir, Billur Barshan. "Detecting Falls with Wearable Sensors Using Machine Learning Techniques", Sensors 2014, 10691-10708

10.
Pallavi Meharia, Dharma P. Agrawal, "The Human Key: Identification and Authentication in Wearable Devices Using Gait," Journal of Information Privacy and Security, 2015

11.
L.Lee, W.E.L. Grimson, "Gait Analysis for Recognition and Classification," Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on

12.
Shu Nishiguich, "Reliability and Validity of Gait Analysis by Android-Based Smartphone," TELEMEDICINE and e-HEALTH, 2012

13.
Sangil Choi, Ik-Hyun Youn, Richelle Lemay, Scott Burns, Jong-Hoon Youn, "Biometric Gait Recognition Based on Wireless Acceleration Sensor Using k-Nearest Neighbor Classification," Computing, Networking and Communications (ICNC), 2014 International Conference