Improving eye movement biometrics in low frame rate eye-tracking devices using periocular and eye blinking features
作者：
Highlights：
• A New approach for continuous driver authentication based on static and dynamic eye features
• Improve eye movement biometrics in the wild using eye blinking dynamic patterns and periocular static features
• Introduce Eye landmarks detection in frames with incomplete faces using a custom-trained machine learning model
• The proposed system can achieve reliable performance using low frame rate eye-tracking devices.
摘要
•A New approach for continuous driver authentication based on static and dynamic eye features•Improve eye movement biometrics in the wild using eye blinking dynamic patterns and periocular static features•Introduce Eye landmarks detection in frames with incomplete faces using a custom-trained machine learning model•The proposed system can achieve reliable performance using low frame rate eye-tracking devices.
论文关键词：Eye movements,Eye blinking,Periocular features,Multi-modal biometrics,Continuous driver authentication
论文评审过程：Received 31 January 2020, Revised 21 September 2020, Accepted 30 January 2021, Available online 6 February 2021, Version of Record 18 March 2021.
论文官网地址：https://doi.org/10.1016/j.imavis.2021.104124
