Proposal and Evaluation of a Gaze Authentication Method that Combines Image Selection and Eye Movement Trajectory Features

In recent years, with the number of users utilizing online services increasing, there is growing demand for more secure authentication methods. Existing authentication methods such as passwords, PINs, and pattern locks, while highly operational and convenient, have issues such as vulnerability to brute force attacks and voyeuristic attacks. As a solution to these problems, gaze authentication has been proposed, where a user draws pre-defined characters or symbols with their gaze, and the features obtained from the trajectory information are used for personal identification. Gaze-based authentication is resistant to attacks such as voyeuristic attacks, thermal attacks, and smudge attacks because it is difficult for a third party to observe the authentication process. However, it also has drawbacks, such as a low authentication success rate and a long authentication time. Therefore, this study proposes a personal authentication system that combines gaze-based image selection and gaze trajectory features to address the issues of authentication rate and authentication time in gaze-based authentication. Additionally, to verify the effectiveness of the proposed system, we collected eye movement trajectory data from users using a gaze detection device, performed data preprocessing and feature extraction, and then calculated the personal authentication rate using several machine learning algorithms. We confirmed that XGBoost had the highest authentication accuracy with an F-measure of 67.5%. Furthermore, in the evaluation of anomaly detection algorithms, Isolation Forest demonstrated superior performance compared to other algorithms.