A Radial basis function neural network-based probabilistic principal component analysis (RBFNN-PPCA) on image recognition based on facial recognition was made. The variational properties of face images are investigated with Eigenfaces algorithm to validate the proposed RBFNN-PPCA algorithm and technique for enhanced optimal image recognition system design. Ten different face image samples for each one hundred different individuals with their corresponding bio-data were taken under different light intensities were cropped and pre-processed. The resulting one thousand face image samples were split into 80% as the training set which constitutes the database of known face images and 20% as the test set which constitutes unknown faces images. Analysis was made on the one thousand face images based on the proposed RBFNN-PPCA algorithm and the Eigenfaces algorithm. The two algorithms were applied simultaneously for enhanced optimal face recognition, and the simulation results show that the proposed face image evaluation techniques as well as the proposed RBF neuroscaling algorithm recognizes a known face image or rejects an unknown face based on the database contents to a high degree of accuracy. The proposed face recognition strategy can be adapted for the design of on-line real-time embedded face recognition systems for public, private, business, commercial or industrial applications.
| Published in | Machine Learning Research (Volume 2, Issue 4) |
| DOI | 10.11648/j.mlr.20170204.16 |
| Page(s) | 152-168 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Eigenfacess, Face Recognition, Neural Network (NN), Neuroscaling, Probabilistic Principal Component Analysis (PPCA), Radial Basis Function (RBF), Singular Value Decomposition (SVD)
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APA Style
Vincent A. Akpan, Joshua B. Agbogun, Michael T. Babalola, Bamidele A. Oluwade. (2017). Radial Basis Function Neuroscaling Algorithms for Efficient Facial Image Recognition. Machine Learning Research, 2(4), 152-168. https://doi.org/10.11648/j.mlr.20170204.16
ACS Style
Vincent A. Akpan; Joshua B. Agbogun; Michael T. Babalola; Bamidele A. Oluwade. Radial Basis Function Neuroscaling Algorithms for Efficient Facial Image Recognition. Mach. Learn. Res. 2017, 2(4), 152-168. doi: 10.11648/j.mlr.20170204.16
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Download@article{10.11648/j.mlr.20170204.16,
author = {Vincent A. Akpan and Joshua B. Agbogun and Michael T. Babalola and Bamidele A. Oluwade},
title = {Radial Basis Function Neuroscaling Algorithms for Efficient Facial Image Recognition},
journal = {Machine Learning Research},
volume = {2},
number = {4},
pages = {152-168},
doi = {10.11648/j.mlr.20170204.16},
url = {https://doi.org/10.11648/j.mlr.20170204.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170204.16},
abstract = {A Radial basis function neural network-based probabilistic principal component analysis (RBFNN-PPCA) on image recognition based on facial recognition was made. The variational properties of face images are investigated with Eigenfaces algorithm to validate the proposed RBFNN-PPCA algorithm and technique for enhanced optimal image recognition system design. Ten different face image samples for each one hundred different individuals with their corresponding bio-data were taken under different light intensities were cropped and pre-processed. The resulting one thousand face image samples were split into 80% as the training set which constitutes the database of known face images and 20% as the test set which constitutes unknown faces images. Analysis was made on the one thousand face images based on the proposed RBFNN-PPCA algorithm and the Eigenfaces algorithm. The two algorithms were applied simultaneously for enhanced optimal face recognition, and the simulation results show that the proposed face image evaluation techniques as well as the proposed RBF neuroscaling algorithm recognizes a known face image or rejects an unknown face based on the database contents to a high degree of accuracy. The proposed face recognition strategy can be adapted for the design of on-line real-time embedded face recognition systems for public, private, business, commercial or industrial applications.},
year = {2017}
}
TY - JOUR T1 - Radial Basis Function Neuroscaling Algorithms for Efficient Facial Image Recognition AU - Vincent A. Akpan AU - Joshua B. Agbogun AU - Michael T. Babalola AU - Bamidele A. Oluwade Y1 - 2017/12/28 PY - 2017 N1 - https://doi.org/10.11648/j.mlr.20170204.16 DO - 10.11648/j.mlr.20170204.16 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 152 EP - 168 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20170204.16 AB - A Radial basis function neural network-based probabilistic principal component analysis (RBFNN-PPCA) on image recognition based on facial recognition was made. The variational properties of face images are investigated with Eigenfaces algorithm to validate the proposed RBFNN-PPCA algorithm and technique for enhanced optimal image recognition system design. Ten different face image samples for each one hundred different individuals with their corresponding bio-data were taken under different light intensities were cropped and pre-processed. The resulting one thousand face image samples were split into 80% as the training set which constitutes the database of known face images and 20% as the test set which constitutes unknown faces images. Analysis was made on the one thousand face images based on the proposed RBFNN-PPCA algorithm and the Eigenfaces algorithm. The two algorithms were applied simultaneously for enhanced optimal face recognition, and the simulation results show that the proposed face image evaluation techniques as well as the proposed RBF neuroscaling algorithm recognizes a known face image or rejects an unknown face based on the database contents to a high degree of accuracy. The proposed face recognition strategy can be adapted for the design of on-line real-time embedded face recognition systems for public, private, business, commercial or industrial applications. VL - 2 IS - 4 ER -
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