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FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

1
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2
School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Current address: East Campus, Sun Yat-sen University, Guangzhou 361024, Guangdong, China.
Electronics 2019, 8(7), 807; https://doi.org/10.3390/electronics8070807
Received: 22 May 2019 / Revised: 4 July 2019 / Accepted: 17 July 2019 / Published: 19 July 2019
(This article belongs to the Special Issue Multidimensional Digital Signal Processing)
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Abstract

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN. View Full-Text
Keywords: face recognition; face pose transformation; generative adversarial networks; key points alignment face recognition; face pose transformation; generative adversarial networks; key points alignment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhuang, W.; Chen, L.; Hong, C.; Liang, Y.; Wu, K. FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition. Electronics 2019, 8, 807.

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