Joint Alignment of Image Faces
Researches on face alignment have made great progress, which benefits from the use of prior information and auxiliary models. However, that information lacks in a single face image has always affected the further development of these researches. The methods considering multiple face images provide a...
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doaj-80e288fba94f41f59613d878c3fe6a6f2021-03-30T02:42:12ZengIEEEIEEE Access2169-35362020-01-01811488411489110.1109/ACCESS.2020.30033329120057Joint Alignment of Image FacesGang Zhang0https://orcid.org/0000-0003-0180-1160Lijia Pan1https://orcid.org/0000-0002-5210-4085Jiansheng Chen2https://orcid.org/0000-0002-1264-7480Yanmin Gong3https://orcid.org/0000-0001-5588-2889Fuyuan Liu4https://orcid.org/0000-0001-8173-0475School of Software, Shenyang University of Technology, Shenyang, ChinaSchool of Software, Shenyang University of Technology, Shenyang, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaDepartment of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USASchool of Software, Shenyang University of Technology, Shenyang, ChinaResearches on face alignment have made great progress, which benefits from the use of prior information and auxiliary models. However, that information lacks in a single face image has always affected the further development of these researches. The methods considering multiple face images provide a feasible way to solve the problem undoubtedly. Joint alignment where multiple face images are considered was presented in the paper. Face alignment was used for each face, and joint face alignment was used for optimizing the alignment results of all faces further. During joint alignment, both rigid variations of faces and non-rigid distortions were considered, however, they were regarded as two independent stages. Joint face alignment was a process where optimization was performed iteratively. In each iteration, both rigid variations and non-rigid distortions were performed sequentially, and moreover, the results of rigid variations were used as input of non-rigid distortions. At the stage of rigid variations, the key points of a face were divided into five groups to reduce the effect of global constraints which was imposed by face shape. After several iterations, the optimal solution of joint alignment can be obtained. The experimental results show that the joint alignment can obtain the optimal results than joint alignment using phased global rigid variations and non-rigid distortions and that using iterative global rigid variations and non-rigid distortions, and it can be used as a novel method for joint alignment.https://ieeexplore.ieee.org/document/9120057/Face analysisface recognitionjoint alignmentface alignment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gang Zhang Lijia Pan Jiansheng Chen Yanmin Gong Fuyuan Liu |
spellingShingle |
Gang Zhang Lijia Pan Jiansheng Chen Yanmin Gong Fuyuan Liu Joint Alignment of Image Faces IEEE Access Face analysis face recognition joint alignment face alignment |
author_facet |
Gang Zhang Lijia Pan Jiansheng Chen Yanmin Gong Fuyuan Liu |
author_sort |
Gang Zhang |
title |
Joint Alignment of Image Faces |
title_short |
Joint Alignment of Image Faces |
title_full |
Joint Alignment of Image Faces |
title_fullStr |
Joint Alignment of Image Faces |
title_full_unstemmed |
Joint Alignment of Image Faces |
title_sort |
joint alignment of image faces |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Researches on face alignment have made great progress, which benefits from the use of prior information and auxiliary models. However, that information lacks in a single face image has always affected the further development of these researches. The methods considering multiple face images provide a feasible way to solve the problem undoubtedly. Joint alignment where multiple face images are considered was presented in the paper. Face alignment was used for each face, and joint face alignment was used for optimizing the alignment results of all faces further. During joint alignment, both rigid variations of faces and non-rigid distortions were considered, however, they were regarded as two independent stages. Joint face alignment was a process where optimization was performed iteratively. In each iteration, both rigid variations and non-rigid distortions were performed sequentially, and moreover, the results of rigid variations were used as input of non-rigid distortions. At the stage of rigid variations, the key points of a face were divided into five groups to reduce the effect of global constraints which was imposed by face shape. After several iterations, the optimal solution of joint alignment can be obtained. The experimental results show that the joint alignment can obtain the optimal results than joint alignment using phased global rigid variations and non-rigid distortions and that using iterative global rigid variations and non-rigid distortions, and it can be used as a novel method for joint alignment. |
topic |
Face analysis face recognition joint alignment face alignment |
url |
https://ieeexplore.ieee.org/document/9120057/ |
work_keys_str_mv |
AT gangzhang jointalignmentofimagefaces AT lijiapan jointalignmentofimagefaces AT jianshengchen jointalignmentofimagefaces AT yanmingong jointalignmentofimagefaces AT fuyuanliu jointalignmentofimagefaces |
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1724184748067127296 |