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), 2024. 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)
| [1] | M. Bryant, “Ten Great Uses of Face Recognition”, Retrieved 19 August, 2015, Available [Online]: http://www.thenextweb.com. |
| [2] | A. Gupta, S. Satkin, A. A. Efros and M. Hebert, “From 3- Scene Geometry to Human Workspace”, The Robotics Institute, Carnegie Mellon University, 1961. |
| [3] | A. J. Goldstein, L. D. Harmon and A. B. Lesk, “Identification of human faces”, In Proceedings of IEEE. May 1971, vol. 59, no 5, 748–760, 1971. |
| [4] | C. E. Chapel, “Fingerprinting: A manual of identification”, 1941. |
| [5] | SONY, “Cyber-shot Compact Digital Cameras”, Sony, 2016. Available [Online]: http://store.sony/.../S_Digital_camera.com. |
| [6] | F. R. Cherrill, “The Fingerprint system”, Scotland Yard (1954). |
| [7] | J. A. Markowitz, “Voice Biometrics”, Communications of the ACM, vol. 43, no. 9, pp. 66, 2000. |
| [8] | R. L. Zunkel, “Hand Geometry Based Verification”, Springer-Verlag, London, 2008. |
| [9] | C. A. Sebastopol, O’Reilly & Associates, Chapter 3, 2000. |
| [10] | P. J. Phillips, H. M. Syed, A. Rizvc and P, J. Rauss, “The FERET evaluation methodology for face recognition algorithms”, IEEE Transactions on Pattern Analysis and Machine intelligence, vol. 22, pp. 1090–1104, 2000. |
| [11] | T. Kwon and J. Lee, “Practical Digital Signature Generation using Biometrics”, Springer, 2010. |
| [12] | MIT, “Why Face Recognition?”, 016, Available [Online]: http://vismod.media.mit.edu/tech-reports/TR-516/node2.html. |
| [13] | G. W. Wilton, “Fingerprints: History, law and Romance”, 1938. |
| [14] | F. Hughes, D. Lichter, R. Oswalo and M. Whitfield, “Face Biometrics: A longitudinal study”, 2006. |
| [15] | T. Ahonen, A. Hadid and M. Pietik, “Face Recognition with Local Binary Patterns”, pp. 469–480, 2004. |
| [16] | P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 17, pp. 711–720, 1997. |
| [17] | S. Bentin, T. Allison, A. Puce, E. Perez and G. McCarthy, “Electrophysiological studies of face perception in humans”, Journal of Cognitive Neuroscience, vol. 8, no. 6, pp. 551–565, 1996. |
| [18] | Y. Chan, S. H. Lin and S. Y. Kung, (1998): Video Indexing and Retrieval”, Multimedia Technology for Applications, Sheu and Ismail editors, IEEE Press, 1998. |
| [19] | V. A. Akpan and R. A. O. Osakwe, “Face Image Processing, Analysis and Recognition Algorithms for Enhanced Optimal Face Recognition Systems Design: A Comparative Study”, African Journal of Computing & ICT, vol. 2, no. 2, pp. 21–40, 2009. |
| [20] | V. Bavel, “Biometrics at the Frontiers: Assessing the Impact on Society”, February 2005. |
| [21] | Y. Wang, T. Tan, and A. K. Jain, “Combining Face and Iris Biometrics for Identity Verification”, In Proceedings of the 4th International Conference on AVBPA, June 2003. |
| [22] | P. H. Lee, G. S. Hsu, T. Chen and Y. P. Hung, “Facial trait code and its application to face recognition”, 2008. |
| [23] | D. Lay, Linear Algebra and It’s Applications. New York: Addison-Wesley, 2000. |
| [24] | L. C. Jain, “Intelligent Biometric Techniques in Face Recognition”, CRC Press, U. S. A., 1999. |
| [25] | A. K. Jain, P. J. Flynn and A. Poss, “Handbook of Biometrics”, Springer–Verlag, London, 2007. |
| [26] | J. Wu, W. A. P. Smith and E. R. Hancock, “Facial gender classification using shape from shading,” Image and Vision Computing, doi: 10.1016/j.imavis.2009.09.003, 2009. |
| [27] | G. C. Littlewort, M. S. Bartlett and K. Lee, “Automatic coding of facial expressions displayed during posed and genuine pain,” Image and Vision Computing, vol. 27, pp. 1797–1803, 2009. |
| [28] | S. Lucey, Y. Wang, M. Cox, M. Sridharan and J. F. Cohn, “Efficient constrained local model fitting for non-rigid face alignment,” Image and Vision Computing, vol. 27, pp. 1804–1813, 2009. |
| [29] | L. I. Smith. (Feb. 26, 2002). “A Tutorial on Principal Component Analysis”, Technical Report”, Available [Online]: http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf. |
| [30] | M. Robinson, M. Escarra, J. Kruegerand and D. Kochelek, “Face Recognition using Eigenfacess”, Course Number: ELEC 301, CONNEXIONS, Rice University, Houston, Texas, 2009. Available [Online]: http://cnx.org/content/col10254/1.2/. |
| [31] | D. J. Duh, J. H. Jeng and S. Y. Chen, “DCT based simple classification scheme for fractal image compression,” Image and Vision Computing, vol. 23, pp. 1115–1121, 2005. |
| [32] | N. Chen, K. Chung and J. Hung, “Novel fractal encoding algorithm using normalized one-norm and kick-out condition”, Image and Vision Computing, (2009), doi: 10.1016/j.imavis.2009.08.007. |
| [33] | P. K. Yalamanchili and B. D. Paladugu, “Comparative Study of Face Recognition Algorithms”, Final Project, ECE847: Digital Image Processing, Department of Electrical Engineering, Clemson University, South Carolina, U. S. A., 2007. Available [Online]: http://www.ces.clemson.edu/~stb/ece847/fall2007/projects/face%20recognition.pdf. |
| [34] | P. N. Belhunmeur, J. P. Hespanha and D. J. Kriegman, “Eigenfacess vs. Fisherfaces: recognition using class specific linear projection”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997. |
| [35] | J. Choi, S. Lee, C. Lee and J. Yi, “A real-time face recognition system using multiple mean faces and dual mode fisherfaces”, IEEE International Symposium on Industrial Electronics (ISIE 2001), pp. 1686–1689, 2001. |
| [36] | A. E. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations”, IEEE Trans. Image Process, vol. 1, no. 1, pp. 18–30, 1992. |
| [37] | T. K. Truong, J. H. Jeng, I. S. Reed, P. C. Lee and A. Q. Li, “A fast encoding algorithm for fractal image compression using the DCT inner product”, IEEE Trans. Image Process, vol. 4, no. 4, pp. 529–535, 2000. |
| [38] | R. Distasi, M. Nappi and D. Riccio, “A range/domain approximation error-based approach for fractal image compression”, IEEE Trans. Image Process, vol. 15, no.1, pp. 89–97, 2006. |
| [39] | H. Bae and S. Kim, “Real-time face detection and recognition using hybrid information extracted from face space and facial features”, Image and Vision Computing, vol. 23, pp. 1181–1191, 2005. |
| [40] | M. A. Turk and A. P. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neurosicence, vol. 3, no. 1, pp. 71–86, 1991. |
| [41] | M. Turk and A. Pentland, “Face recognition using Eigenfaces,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, 1991. |
| [42] | L. Yi-Shin, N. Wai-Seng and L. Chun-Wei, “A Comparison of Different Face Recognition Algorithms”, Technical Report, National Taiwan University, 2009. |
| [43] | J. Park and L. Sandberg, “Universal Approximation Using Radial-Basis-Function Networks” Neural Computation, vol. 3, pp. 246-257, 1991. |
| [44] | M. M. Gupta, L. Jin and N. Homma, “Static and Dynamic Neural Networks: From Fundamental to Advanced Theory”. Hoboken, New Jersey: John Wiley & Sons, 2003. |
| [45] | Haykin, S. (1999). “Neural Networks: A Comprehensive Foundation”. 2nd ed. Upper Saddle River, NJ: Prentice-Hall. |
| [46] | The Math Works Inc., MATLAB & Simulink® R2012a, 3 Maple Drive, California, U. S. A. http://www.mathworks.com. |
| [47] | I. T. Nabney, “Netlab: Algorithm for Pattern Recognition”, Springer-Verlag, London, 2004. |
| [48] | M. E. Tipping and C. M. Bishop, “Probabilistic principal component analysis”, J. Roy. Statist Soc. B 61, 611–622, 1999. |
| [49] | T. Kohonen, “Self-organized formation of topologically correct feature maps”, Biological Cybernetics 43, 59–69, 1982. |
| [50] | N. P. Hughes, “Artefactual Structure from Topographic Mappings”, Master's thesis, Aston University, Birmingham, United Kingdom, 1999. |
| [51] | J. W. Sammon, “A nonlinear mapping for data structure analysis”, IEEE Transactions on Computers, vol. 18, no. 5, pp. 401–409, 1969. |
| [52] | D. Lowe and M. E. Tipping, “Feed-forward neural networks and topographic mappings for exploratory data analysis”, Neural Computing and Applications, 4, 83 – 95, 1996. |
| [53] | M. E. Tipping and D. Lowe, “Shadow targets: A novel algorithm for topographic projections by radial basis functions”, In Proceedings of the IEEE International Conference on Artificial Neural Networks, Vol. 440, pp. 7–12, 1997. |
| [54] | V. A. Akpan and G. D. Hassapis, Training dynamic feedforward neural networks for online nonlinear model identification and control applications, International Reviews of Automatic Control: Theory & Applications, vol. 4, no. 3, pp. 335–350, 2011. |
| [55] | V. A. Akpan, “Development of new model adaptive predictive control algorithms and their implementation on real-time embedded systems”, Ph. D. Dissertation, 517 pages, 2011. [Online] Available: http://invenio.lib.auth.gr/record/127274/files/GRI-2011-7292.pdf. |
| [56] | M. E. Tipping, “Topographic Mappings and Feed-Forward Neural Networks”, Doctoral (Ph.D) thesis, Aston University, Birmingham, United Kingdom, 1996. |
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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