Face Generation for Low-Shot Learning Using Generative Adversarial Networks

Recently, low-shot learning has been proposed for handling the lack of training data in machine learning. Despite of the importance of this issue, relatively less efforts have been made to study this problem. In this paper, we aim to increase the size of training dataset in various ways to improve the accuracy and robustness of face recognition. In detail, we adapt a generator from the Generative Adversarial Network (GAN) to increase the size of training dataset, which includes a base set, a widely available dataset, and a novel set, a given limited dataset, while adopting transfer learning as a backend. Based on extensive experimental study, we conduct the analysis on various data augmentation methods, observing how each affects the identification accuracy. Finally, we conclude that the proposed algorithm for generating faces is effective in improving the identification accuracy and coverage at the precision of 99% using both the base and novel set.