Cluster Computing

, Volume 21, Issue 1, pp 1117–1126 | Cite as

Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition

  • Meijing Li
  • Xiuming Yu
  • Keun Ho Ryu
  • Sanghyuk LeeEmail author
  • Nipon Theera-Umpon


Face recognition is a challenging research field in computer sciences, numerous studies have been proposed by many researchers. However, there have been no effective solutions reported for full illumination variation of face images in the facial recognition research field. In this paper, we propose a methodology to solve the problem of full illumination variation by the combination of histogram equalization (HE) and Gaussian low-pass filter (GLPF). In order to process illumination normalization, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods. Next, a Support Vector Machine classifier is used for face classification. In the experiments, illustration performance was compared with our proposed approach and the conventional approaches with three different kinds of face databases. Experimental results show that our proposed illumination normalization approach (HE_GLPF) performs better than the conventional illumination normalization approaches, in face images with the full illumination variation problem.


Recognition Illumination variation Principal component analysis Support vector machine Illumination normalization 



This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. 2008-0062611) and Basic Science Research Program through the National Research Foundation of Korea (NRF) (No. 2013R1A2A2A01068923) and the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-4009) supervised by the NIPA (National IT Industry Promotion Agency).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Meijing Li
    • 1
  • Xiuming Yu
    • 2
  • Keun Ho Ryu
    • 3
  • Sanghyuk Lee
    • 4
    • 5
    • 6
    Email author
  • Nipon Theera-Umpon
    • 6
    • 7
  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Data PlatformPing An Technology (ShenZhen) Company LimitedPudong New AreaPeople’s Republic of China
  3. 3.Database/Bioinformatics Laboratory, College of Electrical and Computer EngineeringChungbuk National UniversityCheongjuKorea
  4. 4.Xi’an Jiaotong-Liverpool UniversitySuzhouPeople’s Republic of China
  5. 5.Centre for Smart Grid and Information Convergence, XJTLUSuzhouPeople’s Republic of China
  6. 6.Biomedical Engineering CentreChiang Mai UniversityChiang MaiThailand
  7. 7.Electrical EngineeringFaculty of Engineering Chiang Mai UniversityChiang MaiThailand

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