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The Visual Computer

, Volume 33, Issue 11, pp 1483–1493 | Cite as

Illumination-insensitive features for face recognition

  • Yong Cheng
  • Liangbao Jiao
  • Xuehong Cao
  • Zuoyong Li
Original Article

Abstract

Illumination variation is one of the most challenging problems for robust face recognition. In this paper, after investigating the ratio relationship between two neighboring pixels in a digital image, we proposed two illumination-insensitive features, i.e., the non-directional local reflectance normalization (NDLRN) and the fused multi-directional local reflectance normalization (fMDLRN), which not only effectively reduce illumination difference among facial images under different illumination conditions, but also preserve the facial details. Experimental results show that NDLRN and fMDLRN can significantly alleviate the adverse effect of complex illumination on face recognition.

Keywords

Illumination variation Local reflectance normalization Face recognition 

Notes

Acknowledgements

Natural Science Foundation of Jiangsu Province (BK20131342); National Natural Science Foundation of China (NSFC) (61305011); Fuzhou Science and Technology Planning Project (2016-S-116 and 2015-PT-91); Technology Project of Provincial University of Fujian Province (JK2014040); Program for New Century Excellent Talents in Fujian Province University (NCETFJ); Program for Young Scholars in Minjiang University (Mjqn201601); Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467); the Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201712).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Yong Cheng
    • 1
    • 2
    • 3
  • Liangbao Jiao
    • 2
  • Xuehong Cao
    • 2
  • Zuoyong Li
    • 3
  1. 1.School of AutomationSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.School of Communication EngineeringNanjing Institute of TechnologyNanjingPeople’s Republic of China
  3. 3.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouPeople’s Republic of China

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