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

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, 213, 1550 Haigang Avenue, Pudong New Area, Shanghai, People’s Republic of China

    Meijing Li

  2. Department of Data Platform, Ping An Technology (ShenZhen) Company Limited, 316 Laoshan Road, Pudong New Area, Shanghai, People’s Republic of China

    Xiuming Yu

  3. Database/Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, 28644, Korea

    Keun Ho Ryu

  4. Xi’an Jiaotong-Liverpool University, 111, Ren’ai Road Dushu Lake Higher Education Town SIP, Suzhou, 215123, People’s Republic of China

    Sanghyuk Lee

  5. Centre for Smart Grid and Information Convergence, XJTLU, Suzhou, People’s Republic of China

    Sanghyuk Lee

  6. Biomedical Engineering Centre, Chiang Mai University, Chiang Mai, Thailand

    Sanghyuk Lee & Nipon Theera-Umpon

  7. Electrical Engineering, Faculty of Engineering Chiang Mai University, Chiang Mai, 50200, Thailand

    Nipon Theera-Umpon

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  1. Meijing Li
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  2. Xiuming Yu
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Correspondence to Sanghyuk Lee.

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Li, M., Yu, X., Ryu, K.H. et al. Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition. Cluster Comput 21, 1117–1126 (2018). https://doi.org/10.1007/s10586-017-0806-7

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  • DOI: https://doi.org/10.1007/s10586-017-0806-7

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