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Weakly-Supervised Dual Generative Adversarial Networks for Makeup-Removal

  • Xuedong Hou
  • Yun Li
  • Tao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

With the improvement of face recognition precision, face recognition system is used in many fields. However, the face recognition system sometimes cannot recognize the makeup face. In this paper, a new image-to-image translation algorithm based on GAN and dual learning is proposed to remove the makeup. Especially, the proposed algorithm is weakly supervised and it combines the paired and unpaired image-to-image translation model. The dual model is firstly trained using a small number of paired data, then the performance of the model is improved by large number of unpaired data. The proposed weakly-supervised image-to-image translation algorithm is applied into makeup-removal task, and the experimental results demonstrate its higher performance than other algorithms.

Keywords

Image-to-image translation Dual learning GAN Makeup-removal 

Notes

Acknowledgment

This research was partially supported by National Natural Science Foundation of China (NSFC 61603197), Natural Science Foundation of Jiangsu Province (BK20140885) and NUPTSF (NY2141).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer SciencesNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Jiangsu Key Laboratory of Big Data Security and Intelligent ProcessingNanjing University of Posts and TelecommunicationsNanjingChina

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