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Nuclear Norm Regularized Structural Orthogonal Procrustes Regression for Face Hallucination with Pose

  • Dong Zhu
  • Guangwei GaoEmail author
  • Hao Gao
  • Huimin Lu
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

In real applications, the observed low-resolution (LR) face images usually have pose variations. Conventional learning based methods ignore these variations, thus the learned representations are not beneficial for the following reconstruction. In this paper, we propose a nuclear norm regularized structural orthogonal Procrustes regression (N2SOPR) method to learn pose-robust feature representations for efficient face hallucination. The orthogonal Procrustes regression (OPR) seeks an optimal transformation between two images to correct the pose from one to the other. Additionally, our N2SOPR uses the nuclear norm constraint on the error term to keep image’s structural information. A low-rank constraint on the representation coefficients is imposed to adaptively select the training samples that belong to the same subspace as the inputs. Moreover, a locality constraint is also enforced to preserve the locality and the sparsity simultaneously. Experimental results on standard face hallucination databases indicate that our proposed method can produce more reasonable near frontal face images for recognition purpose.

Keywords

Face hallucination Regression analysis Pose variations Low-rank constraint 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant nos. 61502245, 61503195, 61772568, the Natural Science Foundation of Jiangsu Province under Grant no. BK20150849, Research Fund of SKL of Ocean Engineering in Shanghai Jiaotong University (1315;1510), Research Fund of SKL of Marine Geology in Tongji University (MGK1608), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201717). Guangwei Gao is the corresponding author.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of AutomationNanjing University of Posts and TelecommunicationNanjingChina
  2. 2.Institute of Advanced TechnologyNanjing University of Posts and TelecommunicationNanjingChina
  3. 3.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina
  4. 4.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyKitakyushuJapan

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