Face Recognition
Face Generation for Low-shot Learning using Generative Adversarial Networks
People
Junsuk Choe, Song Park, Kyungmin Kim, Dongseob Kim, and Hyunjung Shim
Abstract
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.
Overview of Our Approaches
Data Generation Results
Face Recognition Results
Acknowledgements
This work was supported by ICT R&D program of MSIP/IITP. [ R7124-16-0004, Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding ] and also supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the University Information Technology Research Center support program (IITP-2016-R2718-16-0014) supervised by the IITP (Institute for Information & communication Technology Promotion).
Publication
Face Generation for Low-shot Learning using Generative Adversarial Networks,
J Choe*, S Park*, K Kim*, J Park*, D Kim*, and H Shim. (* equal contribution), International Conference on Computer Vision Workshop (ICCVW), 2017.