Application of Multiple Kernels on PCA Based Program for Face Recognition with Illumination Variations
The application of kernel functions can solve the problem of non-linear image data so that the data can be linearly separable with a hyperplane by mapping the input space to the feature space to increase its dimensions. This article will discuss the improvement in recognition accuracy obtained by implementing multiple kernels in a PCA based program using linear, polynomial, and gaussian kernels for facial recognition with illumination variations. The matching or recognition process is carried out using the SVM method. Improvements obtained from the application of multiple kernels will be compared with the implementation of a single kernel and see how much improvement of the accuracy. Based on the results of the implementation of multiple kernels, the average improvement in accuracy obtained from the face recognition results with illumination variations is 10.5% compared to a single kernel.
A.S. Tolba, A.H. El-Baz, and A.A. El-Harby. 2006. Face Recognition : A Literature Review. International Journal of Signal Processing, Vol.2, No.2, pp.88-103.
Tan, X., S. Chen, Z.-H Zhou, and F. Zhang. 2006. Face Recognition from a Single Image per Person : A Survey. Pattern Recognition, Vol. 39, pp. 1725-1745.
G. Hua, M.-H. Yang, E. Learned-Miller, Y. Ma, M. Turk, D.J. Kriegman, and T. S. Huang. 2011. Introduction to the Special Section on Real-World Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No.10, pp. 1921-1924.
Li, S. Z. and A. K. Jain. 2011. Handbook of Face Recognition. Springer-Verlag London Limited.
Shawe-Taylor, J., N. Christianini. 2004. Kernel Methods for Pattern Analysis.Cambridge University Press: U.S.A.
Schölkopf, B., Smola, A. S., and K-R. Muller, 1998. Nonlinear component analysis as a kernel eigen value problem. Neural Comput. 10(5), 1299–1319.
Gonen, M., E. Alpaydin. 2011. Multiple Kernel Learning Algorithms. Journal of Machine Learning Research, 12, 2211–2268.
Bishop, C. M. 2006. Pattern Recognition and Machine Learning. New York : Springer.
Sonnerburg, S., Ratsch, G., Schafer, C., and Schölkopf, B. 2006. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 7, 1531–1565.
Ming, Z., Bugeau, A., Rouas, J-L., and Shochi, T. 2015. Facial Action Units Intensity Estimation by the Fusion of Features with Multi-kernel Support Vector Machine. 11th International Conference and Workshops on Automatic Face and Gesture Recognition, 1-6
Liu, Q. and Wang, C. 2017. Within-component and between-component multi-kernel discriminating correlation analysis for colour face recognition. IET Journals, Vol. 11, Issue 8, 663-674
Lu, J., Wang, G., and Moulin, P. 2013. Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-Kernel Metric Learning. IEEE International on Conference Computer Vision, 329-336
Copyright (c) 2020 Riko Saragih, Tio Dewantho Sunoto, Judea Janoto Jarden, Dzakki Muhammad Hanif
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article is the property of the author.
- The author acknowledges that the Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) has the right to publish for the first time with a Creative Commons Attribution 4.0 International License.
- The author can enter the writing separately, regulate the non-exculsive distribution of manuscripts that have been published in this journal into other versions (for example: sent to the author's institution respository, publication into books, etc.), by acknowledging that the manuscript was first published in the Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering);