Machine Learning Technique and Normalization Cross Correlation Model Applied for Face Recognition

  • Miriam Amos Adamawa State University, Mubi, Nigeria
  • Ngene C U Adamawa State University, Mubi, Nigeria
  • Manga I Adamawa State University, Mubi, Nigeria
  • Opatoye K I Federal University of Technology Akure, Nigeria
Keywords: Linear Discriminant Analysis (LDA), Open Source Computer Vision (OpenCV), Principal Component Analysis (PCA), Support Vector Machine (SVM), Normalization Cross Correlation (NCC), Region of Interest (ROI).

Abstract

Face recognition systems just like any other biometric systems have continued to stand the test of time as a reliable means of human verification and identification. The high rate of fraud, crime, and terrorism in Nigeria and the world at large makes it increasingly necessary to have recognition systems that will be compatible with security devices currently deployed. However, the accuracy of facial recognition system is dependent on the adequacy of the model applied. This work applies a combination of Support Vector Machine (SVM) and Normalization Cross Correlation (NCC) starting with a preprocessing stage that involves filtering, cropping, normalization as well as histogram equalization of the face images. The facial images were trained and classified using Support Vector Machine then verified by NCC. The experimental study of the model with benchmarked face images showed that the model is very suitable for obtaining a better accuracy level. The False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GAR) and Total Error Rate (TER) values established the superiority of the proposed model over some related ones.

References

Anil, J., Ling, H., and Yatin, K., A multimodal Biometric System using Fingerprint, Face and Speech. International Journal of Computer Applications, (2010)

Aditya Gupta, Madhuri Joshi and Shilpa Metkar,Video Based Real Time Face Recognition System, 2014

Alfatihah, B., A., Face Recognition Using Neural Network, (2010): 14-24

Bolle, R. M., Connell, J. H., Pankanti, S., Ratha, N. K., and Senior, A. W., Guide to biometrics, New York: Springer-Verlag, NewYork, 2004

Douglas Lyon and Nishanth Vincent, Interactive embedded face recognition, Journal of object technology, Online at http://www.jot.fm, Vol. 8, No. 1, January-February, 2009, 1-31

Chanda, B., and Majunmder, D., D., Digital Image Processing and Applications, 1st ed., Prentice Hall of India, (2000).

Deepash Raj Roll, A Realtime Face Recognition system using PCA and various Distance Classfiers, 2011: 1-11

Dunstone, T., and Yager, N., Biometric system and data analysis: Design, evaluation, and data mining. New York: Springer, 2006

Faizan, A., Aaima, N., and Ahmed, Z., Image-based Face Detection and Recognition International Journal of Computer Science Issues, Vol. 9, Issue 6, No 1, November (2012) ISSN (Online): 1694-0814.

Gonzales, R., C. and Woods, R., E., Digital Image Processing. 2nd Edition, Prentice Hall, (2002).

Imran, M., A, Miah, M., S U, and Rahman, H., a face recognition using eigenfaces, 2015

Jain, A. K., Ross A., and Prabhakar, S., An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, (2004): vol. 14, pp. 4–20.

Jie Yang, Hua Yu,William Kunz,An Efficient LDA Algorithm for Face Recognition,School of Computer Science Interactive Systems Laboratories Carnegie Mellon University Pittsburgh, PA 1521,3 1-6

Jain, A. K., Flynn, P., and Ross, A. (2007). Handbook of biometrics, l New York: Springer

Jonathon Phillips, Support Vector Machines Applied to Face Recognition, National Institute of Standards and Technology, jonathon@nist.gov

John D Cook,https://www.johndcook.com/blog/2009/08/24/algorithms-convert-color-grayscale/, 2009

Jain AK, Lu X, Wang Y, Combining Classifiers for Face Recognition, Proceedings of IEEE International Conference on Multimedia and Expo (ICME), Baltimore, MD, 2003, 3:13–16

Müge, C., and Figen Özen, A Face Recognition System Based on Eigen faces Method, (2012): 118 – 123, 2212-0173 pp 1-6

Monwar MD, A Multimodal Biometric System Based on Rank Level Fusion, 2012

Prakash, M., Face Recognition System for Time and Attendance Management in Corporate, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 2018

Panchal, T., and Singh, A., Multimodal Biometric system, International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com, (2013): Volume 3, Issue 5.

Subudhi, K., K and Mishra, R., Human Face Detection and Recognition, Department of Electronics and Communication Engineering National Institute of Technology, Rourkela, (2011):28-38

Published
2019-06-02
How to Cite
Amos, M., C U, N., I, M., & K I, O. (2019). Machine Learning Technique and Normalization Cross Correlation Model Applied for Face Recognition. International Journal of Computer (IJC), 34(1), 1-11. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1408
Section
Articles