Gait-Based Authentication Using Anomaly Detection with Acceleration of Two Devices in Smart Lock
Currently, authentication in Smart locks is performed by fingerprint or face authentication. However, these authentications are inconvenient for smart locks because they require the user to stop for several seconds in front of the door and remove certain accessories (e.g., gloves, sunglasses). In this paper, we propose a user authentication method based on gait features. We propose a system model of gait-based authentication method using accelerometers in a smartphone and a wearable device (i.e., smartwatch), that is robust for unknown data using anomaly detection by machine learning. In addition, we conduct experiment to confirm the authentication rate of the proposed gait-based authentication. As a result, when using Isolation Forest as the anomaly detection algorithm, the average FAR (False Acceptance Rate) was 8.3%, the average FRR (False Rejection Rate) was 9.5%. Furthermore, we found that the better algorithm of anomaly detection of FAR and FRR is different depending on the subjects.
This work was supported by JSPS KAKENHI Grant Numbers JP17H01736, JP17K00139.
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