Deep Learning Classification of Face Images with varying Illumination Conditions

Deep Learning Classification of Face Images with varying Illumination Conditions

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Submitted Apr 18, 2019
Published Apr 30, 2019
DOI https://doi.org/10.24018/ejece.2019.3.3.78
Abstract viewed = 616 times
Issue
Vol. 3 No. 3 (2019)
Section
Articles

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  •   Chinedu Godswill Olebu

    •    Federal University of Technology, Akure, Nigeria.
  •   Jide Julius Popoola

    •    Federal University of Technology, Akure, Nigeria.
  •   Michael Rotimi Adu

    •    Federal University of Technology, Akure, Nigeria.
  •   Yekeen Olajide Olasoji

    •    Federal University of Technology, Akure, Nigeria.
  •   Samson Adenle Oyetunji

    •    Federal University of Technology, Akure, Nigeria.

Abstract

In face recognition system, the accuracy of recognition is greatly affected by varying degree of illumination on both the probe and testing faces. Particularly, the changes in direction and intensity of illumination are two major contributors to varying illumination. In overcoming these challenges, different approaches had been proposed. However, the study presented in this paper proposes a novel approach that uses deep learning, in a MATLAB environment, for classification of face images under varying illumination conditions. One thousand one hundred (1100) face images employed were obtained from Yale B extended database. The obtained face images were divided into ten (10) folders. Each folder was further divided into seven (7) subsets based on different azimuthal angle of illumination used. The images obtained were filtered using a combination of linear filters and anisotropic diffusion filter. The filtered images were then segmented into light and dark zones with respect to the azimuthal and elevation angles of illumination. Eighty percent (80%) of the images in each subset which forms the training set, were used to train the deep learning network while the remaining twenty percent (20%), which forms the testing set, were used to test the accuracy of classification of the deep learning network generated. With three successive iterations, the performance evaluation results showed that the classification accuracy varies from 81.82% to 100.00%.   


Keywords: Anisotropic diffusion filter, Max-Min filters, Learning rate and Accuracy

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How to Cite
[1]
Olebu, C.G., Popoola, J.J., Adu, M.R., Olasoji, Y.O. and Oyetunji, S.A. 2019. Deep Learning Classification of Face Images with varying Illumination Conditions. European Journal of Electrical Engineering and Computer Science. 3, 3 (Apr. 2019). DOI:https://doi.org/10.24018/ejece.2019.3.3.78.