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

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

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 rec...

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Main Authors: Weiwei Zhuang, Liang Chen, Chaoqun Hong, Yuxin Liang, Keshou Wu
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Electronics
Subjects:
face recognition
face pose transformation
generative adversarial networks
key points alignment
Online Access:https://www.mdpi.com/2079-9292/8/7/807
id doaj-a34ac14103c34bb7a6dab2252a06e689
record_format Article
spelling doaj-a34ac14103c34bb7a6dab2252a06e6892020-11-24T21:29:17ZengMDPI AGElectronics2079-92922019-07-018780710.3390/electronics8070807electronics8070807FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face RecognitionWeiwei Zhuang0Liang Chen1Chaoqun Hong2Yuxin Liang3Keshou Wu4School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, ChinaFace 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.https://www.mdpi.com/2079-9292/8/7/807face recognitionface pose transformationgenerative adversarial networkskey points alignment
collection DOAJ
language English
format Article
sources DOAJ
author Weiwei Zhuang
Liang Chen
Chaoqun Hong
Yuxin Liang
Keshou Wu
spellingShingle Weiwei Zhuang
Liang Chen
Chaoqun Hong
Yuxin Liang
Keshou Wu
FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
Electronics
face recognition
face pose transformation
generative adversarial networks
key points alignment
author_facet Weiwei Zhuang
Liang Chen
Chaoqun Hong
Yuxin Liang
Keshou Wu
author_sort Weiwei Zhuang
title FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
title_short FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
title_full FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
title_fullStr FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
title_full_unstemmed FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition
title_sort ft-gan: face transformation with key points alignment for pose-invariant face recognition
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2019-07-01
description 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.
topic face recognition
face pose transformation
generative adversarial networks
key points alignment
url https://www.mdpi.com/2079-9292/8/7/807
work_keys_str_mv AT weiweizhuang ftganfacetransformationwithkeypointsalignmentforposeinvariantfacerecognition
AT liangchen ftganfacetransformationwithkeypointsalignmentforposeinvariantfacerecognition
AT chaoqunhong ftganfacetransformationwithkeypointsalignmentforposeinvariantfacerecognition
AT yuxinliang ftganfacetransformationwithkeypointsalignmentforposeinvariantfacerecognition
AT keshouwu ftganfacetransformationwithkeypointsalignmentforposeinvariantfacerecognition
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