Feature Transfer Learning for Face Recognition With Under-Represented Data
CVPR 2019 | Training with under-represented data leads to biased classifiers in conventionally trained deep networks. We propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples. A Gaussian prior of the variance is assumed across all subjects, and the variance from regular ones is transferred to the under-represented ones. This encourages the under-represented distribution to be closer to the regular distribution. Further, an alternating training regimen is proposed to simultaneously achieve less biased classifiers and a more discriminative feature representation.
Collaborators: Xi Yin, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker