Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition
作者：
Highlights：
• A online approach to unsupervised instance-incremental learning with stream data.
• Adaptation from pseudo-labels, which are the own predictions of the system.
• A strategy to deal with catastrophic forgetting and the effect of wrong pseudo-labels.
• Designed to operate in the open-set, extendable to the class-incremental problem.
• Method for person re-identification based on face without a reservoir of face images.
摘要
•A online approach to unsupervised instance-incremental learning with stream data.•Adaptation from pseudo-labels, which are the own predictions of the system.•A strategy to deal with catastrophic forgetting and the effect of wrong pseudo-labels.•Designed to operate in the open-set, extendable to the class-incremental problem.•Method for person re-identification based on face without a reservoir of face images.
论文关键词：Open-set face recognition,Incremental Learning,Self-updating,Adaptive biometrics,Video-surveillance
论文评审过程：Received 9 December 2020, Revised 20 May 2022, Accepted 2 July 2022, Available online 8 July 2022, Version of Record 18 July 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108885
