18Oct 2017

COMPARATIVE ANALYSIS OF RBF (RADIAL BASIS FUNCTION) NETWORK AND GAUSSIAN FUNCTION IN MULTI-LAYER FEED-FORWARD NEURAL NETWORK (MLFFNN) FOR THE CASE OF FACE RECOGNITION.

  • Qtr No-512, Sector-4, CPWD qtrs, Near Balak Rram Hospital, Timarpur, Delhi India-110054.
Crossref Cited-by Linking logo

  • Abstract
  • Keywords
  • References
  • Cite This Article as
  • Corresponding Author

We know that there are generally two ways in MATLAB software by which we apply radial basis function. 1. Directly use radbas (radial Basis transfer) function in hidden layer of MLFFNN (Multi-layer Feedforward Neural Network). 2. We make Radial Basis network with the help of newrb or newrbe function. If we apply both the function in the case of face recognition then I see that both conditions are not show equal performance, second condition is fast with respect to first condition. Because second condition is used for local approximation and first condition is used for universal approximations. For recognition of face I take Caltech 101 database. Caltech 101 database is created in California Institute of Technology in September 2003. This database contains color-full digital images. This database is compiled by Fei-Fei Li, Marco Andreetto, Marc ?Aurelio Ranzato and PietroPerona. This is used for facilitate computer vision research and technique. This is mainly used for image recognition, classification and categorization. Caltech 101 contains 9146 images. All the images are spilt into 101 distinct object categories such as faces, watches, ants etc.

Reference Research Papers:-
  1. Thakur, J. K. Sing and others, ?Face Recognition using Principal Component Analysis and RBF Neural Networks?, First International Conference on Emerging Trends in Engineering and Technology, Page(s):695 ? 700, 2008 IEEE.
  2. KhairulAzha A. Aziz and other, ?Face Detection using Radial Basis Function Neural Networks with variance Spread Value? , 2009 International Conference on Soft Computing and Pattern Recognition, Page(s):399 ? 403, 2009 IEEE.
  3. Yong Zhang and ZhiMaoXue, ? RBF Neural Network Application to Face Recognition?, 2010 International Conference on Challenges in Environment Science and Computer Engineering, Page(s):381 ? 384, 2010 IEEE.
  4. Mathew Turk and Alex Pentland, ?Eigenfaces for Recognition?, Journal of Cogitive Neurosciences, Volume-3.
  5. Nandini, P.Bhargavi, G. Raja Sekhar, ?Face Recognition using Neural Networks?, International Journal of Scientific and Research Publications, Volume 3, Issue 3, March 2013 ISSN-2250-3153.
  6. Ajeet Singh, Bhupesh Bhatia, VijayRajShokeen and Dr. B.K.Singh, ?Comparison of PCA,LDA, ICA, SVM & HGPP?, International Conference on Advances in Electronics, Electrical and Computer Science Engineering-EEC 2012.
  7. Member, IEEE, Shiqian Wu and Jumwi Lu, ?Face Recognition using RBF Neural Networks?, International Conference on Decision & Control Phoenix, USA, December 1999, Page(s): 2162 ? 2167, 1999 IEEE.
  8. Sue Inn Ch?ng, KahPhoolSeng, Li-MinnAng ?Modular Dynamic RBF Neural Network for Face Recognition?, 2008 IEEE.
  9. VandanaAgarwal and SurekhaBhanot. ?Evolutionary Design of Multiquadric Radial Basis Functions Neural Network for Face Recognition?, Birla Institute of Technology and Science, Pilani India, IEEE.
  10. Virgindia Espinosa-Duro, Electronic and Automatic Department, ?Biometric Identification System using a Radial Basis Network? Page(s): 47 ? 51, 2000 IEEE.
  11. Kalpana C. Jondhale and Dr. L.M. Waghmare, ?Improvement in PCA performance using FLD and RBF Neural Networks for Face Recognition?, Third International Conference on Emerging Trends in Engineering and Technology, Page(s):500 ? 505, 2010 IEEE
  12. Gabor Feature Selection and Improvement Radial Basis Function Networks for Facial Expression Recognition? ? 2010 IEEE.
  13. Marcos Faundez-Zanuy and Enric Monte-Moreno, ?Face Recognition using a Radial Basis Function Classifier?, ? 2006 IEEE.
  14. Sue Inn Ch?ngKahPhoolSeng and Li-MinnAng, ?Modular Dynamic RBF Neural Network for Face Recognition?, ? IEEE.
  15. Thai Hoang Le, ?Research Article on Applying Artificial Neural Networks for Face Recognition?, ? IEEE.
  16. Nilind Sharma and Asst. Prof. Shiv Kumar Dubey, ?Face Recognition Analysis Using PCA, ICA and Neural Network?, International Journal of Digital Application & Contemporary research, volume 2, Issue 9, April 2014.
  17. Digital Color Image Processing- Andreas Koschan&MongiAbidi, Publication-Wiley Interscience, A John Wiley & Sons, Inc. Publication.
Reference Books:-
  1. Digital Color Image Processing- Andreas Koschan&MongiAbidi, Publication-Wiley Interscience, A John Wiley & Sons, Inc. Publication.
  2. Image Processing with MATLAB- Omer Demirkaya, Musa HakanAsyali&PrasannaK.sahoo , Publication- CRC Press London New York.
  3. MATLAB for Neuroscientists-Pascal wallisch& Michael lusignan et al publication-Elsevier
  4. Neural Networks, A Comprehensive Foundation- Simon Haykin
  5. Neural Network Design- Martin T. Hagan
  6. Artificial Neural Networks- B. Yegnanarayana
  7. Handbook of Face Recognition, 2nd Edition, Springer publication
Reference Internet sites:-

[Arvind Kumar. (2017); COMPARATIVE ANALYSIS OF RBF (RADIAL BASIS FUNCTION) NETWORK AND GAUSSIAN FUNCTION IN MULTI-LAYER FEED-FORWARD NEURAL NETWORK (MLFFNN) FOR THE CASE OF FACE RECOGNITION. Int. J. of Adv. Res. 5 (Oct). 863-873] (ISSN 2320-5407). www.journalijar.com

ARVIND KUMAR

DOI:

Article DOI: 10.21474/IJAR01/5597      
DOI URL: https://dx.doi.org/10.21474/IJAR01/5597

Download Full Paper

Download PDF No. of Downloads: 38 | No. of Views: 185