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• W1915113127 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%)." @default.
• W1915113127 created "2016-06-24" @default.
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• W1915113127 date "2015-06-01" @default.
• W1915113127 modified "2023-10-10" @default.
• W1915113127 title "An unsupervised approach for gait-based authentication" @default.
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• W1915113127 doi "https://doi.org/10.1109/bsn.2015.7299423" @default.
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