# Feature Transfer Learning for Deep Face Recognition with Under-Represented Data

## TL;DR

This paper introduces a feature transfer learning framework that enhances deep face recognition for under-represented subjects by transferring variance from well-represented classes, leading to less biased classifiers and improved recognition performance.

## Contribution

The proposed center-based feature transfer method and alternating training regimen effectively augment under-represented class features, reducing bias and improving face recognition accuracy.

## Key findings

- Improved accuracy on LFW, IJB-A, and MS-Celeb-1M datasets.
- Successful visual interpolation demonstrating disentanglement of identity and variations.
- Effective augmentation of under-represented class feature spaces.

## Abstract

Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such significant portion results in insufficient training data. Training with under-represented data leads to biased classifiers in conventionally-trained deep networks. In this paper, 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 are 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. We conduct ablative study to mimic the under-represented datasets by varying the portion of under-represented classes on the MS-Celeb-1M dataset. Advantageous results on LFW, IJB-A and MS-Celeb-1M demonstrate the effectiveness of our feature transfer and training strategy, compared to both general baselines and state-of-the-art methods. Moreover, our feature transfer successfully presents smooth visual interpolation, which conducts disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations such as pose and lighting.