Learning from weakly labeled faces and video in the wild
作者：
Highlights：
• A probabilistic sparse kernel technique able to learn with noisy labels.
• Method consistently boosts performance on real world face recognition tasks such as the Labeled Faces in the Wild evaluation.
• Experiments show the importance of using non-linear classifiers when data is weakly labeled.
• The method can learn from weakly annotated video where the subject׳s face is not always present.
摘要
Highlights•A probabilistic sparse kernel technique able to learn with noisy labels.•Method consistently boosts performance on real world face recognition tasks such as the Labeled Faces in the Wild evaluation.•Experiments show the importance of using non-linear classifiers when data is weakly labeled.•The method can learn from weakly annotated video where the subject׳s face is not always present.
论文关键词：Semi-supervised learning,Face recognition,Graphical models
论文评审过程：Received 16 May 2013, Revised 22 August 2014, Accepted 18 September 2014, Available online 30 September 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.09.016
