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An unsupervised approach for gait-based authentication

3 years 10 months ago
An unsupervised approach for gait-based authentication
—Similar to fingerprint and iris pattern, everyone’s gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the user’s gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subject’s trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).
Guglielmo Cola, Marco Avvenuti, Alessio Vecchio, G
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where BSN
Authors Guglielmo Cola, Marco Avvenuti, Alessio Vecchio, Guang-Zhong Yang, Benny Lo
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