A Face Recognition System Based on Principal Component Analysis-Wavelet and Support Vector Machines
Abstract
Face recognition can represent a key requirement in various types of applications such as human-computer interface, monitoring systems, as well as personal identification. In this paper, design and implement of face recognition system are introduced. In this system, a combination of principal component analysis (PCA) and wavelet feature extraction algorithms with support vector machine (SVM) and K-nearest neighborhood classifier is used. PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used as a classifier to compare its results with the proposed system. For a more comprehensive comparison, two face image databases (Yale and ORL) are used to test the performance of the system. Finally, the experimental results show the efficiency and reliability of face recognition system, and the results demonstrate accuracy on two databases indicated that the results enhancement 5% using the SVM classifier with polynomial Kernel function compared to use feedforward neural network classifier.
Downloads
References
S. Gupta, O. P. Sahu, R. Gupta and A. Goel. “A bespoke approach for face-recognition using PCA”. International Journal of Computer Science and Engineering, Vol. 2, no. 02, pp.155-158, 2010.
M. David. “Reversible Integer to Integer Wavelet Transforms for Image Coding”. Thesis, University of British Columbia, 2002.
J. S. Taneja. “Analysis of E. coli Promoters Using Support Vector Machine”. Thesis, Thapar Institute of Engineering and Technology, 2006.
M. E. Mavroforakis. “Geometric Approach to Statistical Learning Theory Through Support Vector Machines (SVM) with Application to Medical Diagnosis”. Thesis, National and Kapodistrian University of Athens, 2008.
X. Wu, V. Kumar, J. R. Quinlan and J. Ghosh. “Top 10 algorithms in data mining”. Knowledge and Information Systems, vol. 14, no. 1, pp. 1-37, 2008.
M. Zuhaer and N. Al-Dabagh. “Face recognition using LBP, FLD and SVM with single training sample per person”. International Journal of Scientific and Engineering Research, vol. 5, no. 5, pp. 180, May 2014.
R. O. Duda, P. E. Hart and D. G. Stork. “Pattern Classification”. 2nd ed. New York: John Wiley and Sons, Inc., 2000.
P. N. Bellhumer, J. Hespanha and D. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
F. Samaria and A. Harter. D. Parameterisation of a Stochastic Model for Human Face Identificatio. Conference: Applications of Computer Vision. Proceedings of the Second IEEE Workshop, 1994.
Copyright (c) 2019 Laith R. Fleah, Shaimaa A. Al-Aubi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
