We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Face Recognition using Extended Kalman Filter based Machine Learning

by Kanchan Singh, Ashok K Sinha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 16
Year of Publication: 2013
Authors: Kanchan Singh, Ashok K Sinha
10.5120/11172-6461

Kanchan Singh, Ashok K Sinha . Face Recognition using Extended Kalman Filter based Machine Learning. International Journal of Computer Applications. 66, 16 ( March 2013), 43-50. DOI=10.5120/11172-6461

@article{ 10.5120/11172-6461,
author = { Kanchan Singh, Ashok K Sinha },
title = { Face Recognition using Extended Kalman Filter based Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 16 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number16/11172-6461/ },
doi = { 10.5120/11172-6461 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:22:37.442694+05:30
%A Kanchan Singh
%A Ashok K Sinha
%T Face Recognition using Extended Kalman Filter based Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 16
%P 43-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years there has been a growing concern by researchers in developing algorithm for face recognition. The proposed work addresses the problem of face recognition in still images using Extended Kalman Filter for machine learning. The algorithm comprises of designing a feature vector which has discrete wavelet coefficients of the face and, a coefficient representing parameters of the face. Global features of the face are captured by wavelet coefficients and the local feature of the face is captured by facial parameter. The coefficients of the feature vector are used as inputs to the recurrent neural network using EKF algorithm for training. . The proposed algorithm has been tested on various real images and its performance is found to be quite satisfactory when compared with the performance of conventional methods of face recognition such as the Eigen-face method.

References
  1. Matthew Turk and Alex Pentland, "Eigenfaces for face recognition," Journal of Cognitive Neuroscience, vol. 3, number1 (1991) pp. 71–86.
  2. Rajkiran Gottumukkal, Vijayan K. Asari, "An improved face recognition technique based on modular PCA approach," Pattern Recognition Letters 25 (2004) 429–436.
  3. Guan-Chun Luh and Ching-Chou Hsieh, Face Recognition Using Immune Network Based on Principal Component Analysis, in Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09, June 12-14, 2009, Shanghai, China DOI= http://doi. acm. org/10. 1145/1543834. 1543888.
  4. Zhao, W. , Chellappa, R. ,Rosenfeld A. , Phillips P. J. , "Face Recognition: A Literature Survey," ACM Computing Survey 35 (2003) 399–458.
  5. Jin, Z. , Yang, J. Y. , Hu, Z. S. , Lou, Z, "Face Recognition Based on the Uncorrelated Discriminant Transformation," Pattern Recognition 34 (2001) 1405–1416.
  6. Zhongkai Han, Chi Fang and Xiaoqing Ding, A Discriminated Correlation Classifier for Face Recognition, in proceedings of the 2010 ACM Symposium on Applied Computing, SAC'10 March22-26, 2010,Sierre, Switzerland. DOI=http://doi. acm. org/10. 1145/1774088. 1774406.
  7. Lamiaa Mostafa and Sherif Abdelazeem, "Face Detection Based on Skin Color Using Neural Networks," in GVIP 05 Conference, 19-21 December 2005, CICC, Cairo, Egypt.
  8. Cahi, D. and Ngan, K. N. , "Face Segmentation Using Skin-Color Map in Videophone Applications," IEEE Transaction on Circuit and Systems for Video Technology, Vol. 9, pp. 551-564 (1999).
  9. Crowley, J. L. and Coutaz, J. , "Vision for Man Machine Interaction," Robotics and Autonomous Systems, Vol. 19, pp. 347-358 (1997).
  10. Kjeldsen, R. and Kender. J. , "Finding Skin in Color Images," in proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 312-317 (1996).
  11. Sanjay Kr. Singh1, D. S. Chauhan2 et. al. , "A Robust Skin Color Based Face Detection Algorithm," Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234 (2003).
  12. Yuehui Chen and Yaou Zhao, "Face Recognition Using DCT and Hierarchical RBF Model," IDEAL 2006, LNCS 4224, pp. 355–362, 2006, Springer-Verlag Berlin Heidelberg 2006.
  13. Fabrizia M. de S. Matos, Leonardo V. Batista and JanKees v. d. Poel, Face Recognition Using DCT Coefficient Selection, In proceedings of the 2008 ACM symposium on Applied computing SAC'08, March 16-20, 2008, Fortaleza,Ceará,Brazil.
  14. Øyvind Ryan, "Applications of the wavelet transform in image Processing," Sponsored by the Norwegian Research Council, project nr. 160130/V30.
  15. Cunjian. chen and Jiashu. zhang, "Wavelet Energy Entropy as a New Feature Extractor for Face Recognition," in Fourth International Conference on Image and Graphics, 2007 © IEEE DOI 10. 1109/ICIG. 2007. 60.
  16. M. K. Bhowmik, D. Bhattacharjee, M. Nasipuri, D. K. Basu & M. Kundu, "Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition – A Comparative Study," International Journal of Image Processing (IJIP), Volume (4), Issue (1), pp. 12-23.
  17. Boqing Gong, Yueming Wang, Jianzhuang Liu and Xiaoou Tang, Automatic Facial Expression Recognition on a Single 3D Face by Exploring Shape Deformation, in proceedings of the seventeen ACM international conference on Multimedia, MM'09, October 19–24, 2009 Beijing China, DOI= http://doi. acm. org/10. 1145/1631272. 1631358.
  18. N. Sudha, A. R. Mohan and Pramod K. Meher, 2011. A Self- Configurable Systolic Architecture for Face Recognition System Based on Principal Component Neural Network, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 21, No. 8, August 2011, pp. 1071-1084.
  19. Wonjun Hwang, Haitao Wang, Hyunwoo Kim, Seok-Cheol Kee, and Junmo Kim,2011. Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation, IEEE Transactions on Image Processing,Vol. 20, No. 4,April 2011, pp. 1152-1165.
  20. Arindam Biswas, Suman Khara and Partha Bhowmick. Extraction of Regions of Interest from Face Images using Cellular Analysis. In Proceedings of the 1st Bangalore Annual Compute Conference, Bangalore, Karnataka, India, Jan18-20, Compute2008 DOI=http://doi. acm. org/10. 1145/1341771. 1341787.
  21. Bart Kroon, Alan Hanjalic and Sander M. P. Maas, Eye Localization for Face Matching: Is It Always Useful and Under What Conditions?, In Proceedings of the 2008 international conference on Content-based image and video retrieval, CIVR'08, July 7–9, 2008, Niagara Falls, Ontario, Canada. DOI=http://doi. acm. org/10. 1145/1386352. 1386401
  22. Jun-ying zeng, Jun-ying gan, Yi-kui zha. 2012. A Novel Partially Occluded Face Recognition Method Based On Biomimetic Pattern Recognition, Proceedings of the 2012 IEEE International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July, 2012, pp. 175-179.
  23. Brian Cheung. 2012. Convolutional Neural Networks applied to Human Face Classification, 11th IEEE International Conference on Machine Learning Applications, pp 580-583.
  24. Symon Haykin, Neural Networks, Pearson Education, Second Edition, pp. 784-790.
  25. Amara Graps. An Introduction to Wavelets. in IEEE Computational Science and Engineering, Summer 1995, vol. 2, num. 2, published by the IEEE Computer Society, 10662 Los Vaqueros Circle, Los Alamitos, CA 90720, USA.
  26. Mallat, S. (1989), "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Pattern Anal. and Machine Intell. , vol. 11, no. 7, pp. 674-693.
  27. Y. Meyer, "Wavelets: Algorithms and Applications," Society for Industrial and Applied Mathematics, Philadelphia,1993, pp. 13-31, 101-105.
  28. A. Haar. Zur Theorie der orthogonalen Funktionensysteme (German). Mathematische Annalen 69 (1910), no. 3, 331–371.
Index Terms

Computer Science
Information Sciences

Keywords

Principal Component Analysis (PCA) Eigen-Face Method Haar Wavelet Extended Kalman Filter (EKF) Discrete Wavelet Transform (DWT) Discrete Cosine Transformaion (DCT) Wavelet Facial Parameter Recurrent Neural Network (RNN) Artificial Neural Network (ANN)