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Efficient 3D reconstruction for face recognition

Authors:

Hongjiang Zhang,

Wen GaoAuthors Info & Claims

Pattern Recognition, Volume 38, Issue 6

Pages 787 - 798

https://doi.org/10.1016/j.patcog.2004.11.004

Published: 01 June 2005 Publication History

Abstract

Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient two-dimensional (2D)-to-three-dimensional (3D) integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and (3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.

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Cited By

Huang YChen XKim JYang HLi CYang JChen DWilliams BChen YNeville J(2023)FreeEnricherProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i1.25176(962-970)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i1.25176
Correia JMartins TRebelo SBicker JMachado P(2021)The X-Faces Behind the Portraits of No OneSN Computer Science10.1007/s42979-021-00604-w2:4Online publication date: 24-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00604-w
Petpairote CMadarasmi SChamnongthai K(2021)2D Pose-Invariant Face Recognition Using Single Frontal-View Face DatabaseWireless Personal Communications: An International Journal10.1007/s11277-020-07063-1118:3(2015-2031)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11277-020-07063-1
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Efficient 3D reconstruction for face recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Computer graphics
2. Networks
  1. Network protocols

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 38, Issue 6

June, 2005

162 pages

ISSN:0031-3203

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Copyright © Pattern Recognition Society © 2004.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 June 2005

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Cited By

Huang YChen XKim JYang HLi CYang JChen DWilliams BChen YNeville J(2023)FreeEnricherProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i1.25176(962-970)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i1.25176
Correia JMartins TRebelo SBicker JMachado P(2021)The X-Faces Behind the Portraits of No OneSN Computer Science10.1007/s42979-021-00604-w2:4Online publication date: 24-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00604-w
Petpairote CMadarasmi SChamnongthai K(2021)2D Pose-Invariant Face Recognition Using Single Frontal-View Face DatabaseWireless Personal Communications: An International Journal10.1007/s11277-020-07063-1118:3(2015-2031)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s11277-020-07063-1
Devi PBaskaran R(2021)SL2E-AFRE : Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimationApplied Intelligence10.1007/s10489-020-02000-y51:4(2253-2268)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10489-020-02000-y
Abdulwahab SRashwan HGarcía MJabreel MChambon SPuig D(2020)Adversarial Learning for Depth and Viewpoint Estimation From a Single ImageIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.297306830:9(2947-2958)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1109/TCSVT.2020.2973068
Ammar SBouwmans TZaghden NNeji M(2020)Towards an Effective Approach for Face Recognition with DCGANs Data AugmentationAdvances in Visual Computing10.1007/978-3-030-64556-4_36(463-475)Online publication date: 5-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-64556-4_36
Zhang ZChen XWang BHu GZuo WHancock E(2019)Face Frontalization Using an Appearance-Flow-Based Convolutional Neural NetworkIEEE Transactions on Image Processing10.1109/TIP.2018.288355428:5(2187-2199)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1109/TIP.2018.2883554
Zhang JZhu C(2019)Approach to 3D face reconstruction through local deep feature alignmentIET Computer Vision10.1049/iet-cvi.2018.515113:2(213-223)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5151
Zhang JZhao XKan MShan SChai XChen X(2019)Locality-constrained framework for face alignmentFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-6617-z13:4(789-801)Online publication date: 1-Aug-2019
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Wang KChen ZWu QLiu C(2019)Face recognition using AMVP and WSRC under variable illumination and poseNeural Computing and Applications10.1007/s00521-017-3316-x31:8(3805-3818)Online publication date: 1-Aug-2019
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