An unsupervised approach for gait-based authentication | IEEE Conference Publication | IEEE Xplore

An unsupervised approach for gait-based authentication


Abstract:

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 lig...Show More

Abstract:

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%).
Date of Conference: 09-12 June 2015
Date Added to IEEE Xplore: 19 October 2015
Electronic ISBN:978-1-4673-7201-5

ISSN Information:

Conference Location: Cambridge, MA, USA

References

References is not available for this document.