High Quality Facial Data Synthesis and Fusion for 3D Low-quality Face Recognition

Shisong Lin, Changyuan Jiang, Feng Liu, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-The-Art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.

Original languageEnglish
Title of host publication2021 IEEE International Joint Conference on Biometrics, IJCB 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665437806
DOIs
Publication statusPublished - 4 Aug 2021
Externally publishedYes
Event2021 IEEE International Joint Conference on Biometrics, IJCB 2021 - Shenzhen, China
Duration: 4 Aug 20217 Aug 2021

Publication series

Name2021 IEEE International Joint Conference on Biometrics, IJCB 2021

Conference

Conference2021 IEEE International Joint Conference on Biometrics, IJCB 2021
Country/TerritoryChina
CityShenzhen
Period4/08/217/08/21

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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