Learning from weakly labeled faces and video in the wild
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.
face recognition semi-supervised learning graphical models