Manifold learning for user profiling and identity verification using motion sensors
作者：
Highlights：
• A solution for learning identify-related motion traits using user-agnostic models.
• The proposed manifold-based solution outperformed previous, user-specific ones.
• The proposed solution presented robustness in cross-dataset and open-set scenarios.
• The proposed solution employs a small amount of target user data for personalization.
• An extended dataset with motion data from 115 users is made available.
摘要
•A solution for learning identify-related motion traits using user-agnostic models.•The proposed manifold-based solution outperformed previous, user-specific ones.•The proposed solution presented robustness in cross-dataset and open-set scenarios.•The proposed solution employs a small amount of target user data for personalization.•An extended dataset with motion data from 115 users is made available.
论文关键词：User profiling,Motion sensor,Gait,Manifold learning,Open-set user profiling,Cross-dataset user profiling
论文评审过程：Received 6 June 2019, Revised 31 January 2020, Accepted 28 April 2020, Available online 19 May 2020, Version of Record 19 May 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107408
