Neuro-Fuzzy Hybridization using Modified S Membership Function and Kernel Extreme Learning Machine for Robust Face Recognition under Varying Illuminations
DOI:
https://doi.org/10.4108/eai.13-7-2018.163575Keywords:
Illumination normalization, S membership function, face recognition, KELMAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Scalable Information Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.