sis 18: e4

Research Article

Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations

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  • @ARTICLE{10.4108/eai.13-7-2018.163575,
        author={Virendra P. Vishwakarma and Sahil Dalal},
        title={Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={3},
        keywords={Illumination normalization, S membership function, face recognition, KELM},
        doi={10.4108/eai.13-7-2018.163575}
    }
    
  • Virendra P. Vishwakarma
    Sahil Dalal
    Year: 2020
    Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163575
Virendra P. Vishwakarma1, Sahil Dalal1,*
  • 1: University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi-110078
*Contact email: dalalsahil22@yahoo.co.in

Abstract

The multifaceted light varying environment severely degrades the performance of person recognition using facial images. Here, the authors present a novel person identification method using hybridization of artificial neural network (ANN) and fuzzy logic concepts. An efficient illumination normalization method is presented with the help of a new modified S membership function. The proposed method of illumination normalization retains the large scale facial features as well as suppresses the variations related to change in light variations. Kernel extreme learning machine (KELM) which is a nonlinear and non-iterative learning algorithm of ANN is used for classification. Various kernel types and parameters are experimented to find the best choice for robust classification. To assess the performance of proposed hybridization, Yale and extended Yale B face databases have been used. Very promising results have been achieved which establish the worth of the proposed method.