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Frontiers of Computer Science

, Volume 12, Issue 6, pp 1173–1191 | Cite as

Fusing magnitude and phase features with multiple face models for robust face recognition

  • Yan Li
  • Shiguang Shan
  • Ruiping Wang
  • Zhen Cui
  • Xilin Chen
Research Article
  • 13 Downloads

Abstract

High accuracy face recognition is of great importance for a wide variety of real-world applications. Although significant progress has been made in the last decades, fully automatic face recognition systems have not yet approached the goal of surpassing the human vision system, even in controlled conditions. In this paper, we propose an approach for robust face recognition by fusing two complementary features: one is Gabor magnitude of multiple scales and orientations and the other is Fourier phase encoded by spatial pyramid based local phase quantization (SPLPQ). To reduce the high dimensionality of both features, block-wise fisher discriminant analysis (BFDA) is applied and further combined by score-level fusion. Moreover, inspired by the biological cognitive mechanism, multiple face models are exploited to further boost the robustness of the proposed approach. We evaluate the proposed approach on three challenging databases, i.e., FRGC ver2.0, LFW, and CFW-p, that address two face classification scenarios, i.e., verification and identification. Experimental results consistently exhibit the complementarity of the two features and the performance boost gained by the multiple face models. The proposed approach achieved approximately 96% verification rate when FAR was 0.1% on FRGC ver2.0 Exp.4, impressively surpassing all the best known results.

Keywords

face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization 

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Notes

Acknowledgements

This work is partially supported by the National Basic Research Program of China (2015CB351802), the National Natural Science Foundation of China (Grant Nos. 61390511, 61222211, 61379083, 61271445), the Strategic Priority Research Program of the CAS (XDB02070004), and the Youth Innovation Promotion Association CAS (2015085).

Supplementary material

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yan Li
    • 1
    • 2
  • Shiguang Shan
    • 1
    • 2
  • Ruiping Wang
    • 1
    • 2
  • Zhen Cui
    • 3
  • Xilin Chen
    • 1
    • 2
  1. 1.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology (ICT), CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina

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