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Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 12
Year of Publication: 2014
Amrita Biswas
M. K Ghose
Moumee Pandit

Amrita Biswas, M K Ghose and Moumee Pandit. Article: Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition. International Journal of Computer Applications 96(12):25-31, June 2014. Full text available. BibTeX

	author = {Amrita Biswas and M. K Ghose and Moumee Pandit},
	title = {Article: Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {12},
	pages = {25-31},
	month = {June},
	note = {Full text available}


In this paper neural network classifier is applied on transformed shape features for face recognition. Classification by neural networks to a large extent depends on the neural network architecture. We have investigated three different neural network architectures for classification namely-Feed Forward Neural Network, Cascade Feed Forward Neural Network and Radial Basis Function Neural Network and tested their performance for three sets of feature extracted data. For feature extraction we convert the 2-D gray level face images into their respective depth maps or physical shape which are subsequently transformed by three different methods to get three separate data sets,namely-Coiflet Packet , Radon Transform and Fourier Mellin Transform to compute energy for feature extraction. After feature extraction each of the training classes are optimally separated using linear discriminant analysis. The neural network classifiers have been tested on each of the three sets of feature extracted data and a comparative analysis has been done on the results obtained. The proposed algorithms have been tested on the ORL database, widely used for face recognition experiments.


  • Rabia Jafri and Hamid R Arabina, "A survey of Face Recognition Techniques",Journal of Information Processing Systems,Vol. 5,No. 2,June 2009
  • W. Zhao, R. Chellappa, P. J. Phillips, "A. Rosenfeld,FaceRecognition:A Literature Survey",ACM Computing Surveys, Vol. 35, No. 4,pp. 399-458. ,2003
  • Hyeonjoon Moon, P Jonathon Phillips,"Computational and Performance Aspects of PCA Based Face Recognition Algorithms", Perception 30(3),pp. 303 - 321,2001
  • Meftah Ur Rahman, "A comparative study on Face Recognition Systems and Neural networks"Computer Vision and Pattern Recognition,arXiv:1210. 1916v1,2012
  • P. Latha,Dr. L. Ganesan,Dr. S. Annadurai, "Face Recognition using Neural networks", Signal Processing: An International Journal (SPIJ) Volume (3) : Issue (5),pp 153-160
  • Anissa Bouzalmat, NaouarBelghini, ArsalaneZarghili, Jamal Kharroubi&AichaMajda,"Face Recognition using Neural network based Fourier Gabor Filters & Random Projection",International Journal of Computer Science and Security,(IJCSS),Vol. 5,Issue. 3,2011
  • V. BalamuruganMukundan Srinivasan Vijayanarayanan. A,"A New Face Recognition Technique using Gabor Wavelet Transform and Back Propagation Network",International Journal of Computer Applications,Vol. 49,No. 3,2012
  • Sambhunath Biswas and Amrita Biswas,"Face Recognition Algorithms Based on Transformed Shape Features", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 3, No 3, pp-445-451,May 2012
  • Sambhunath Biswas and Amrita Biswas,"FourierMellin Transform Based Face Recognition',International Journal of Computer Engineering and Technology(IJCET),vol. 4,issue 1,pp8-15,2013
  • Ping-Sing Tsai and Mubarak Shah "Shape From Shading Using Linear Approximation", Image and Vision Computing,vol:12, pp. 487-498,1994
  • B. K. P Horn," Robot Vision", Cambridge,Massachusetts, USA ,MIT Press,1986.
  • R. C. Gonzalez and R. E. woods," Digital Image Processing", Dorling Kindersley, India, Pearson Prentice Hall, 2006.
  • C. SydneyBurrus and A. Gopinath and HaitaoGuo,"Introduction to Wavelets and Wavelet Transforms",Prentice Hall, N. J 07458, USA, 1998.
  • St´ephaneDerrode,Robust and Efficient Fourier–Mellin Transform Approximations for Gray-Level Image Reconstruction and Complete Invariant escription Computer Vision and Image Understanding 83, 57–78 (2001)
  • KouroshJafari-Khouzani and Hamid Soltanian-Zadeh,RadonTransformOrientation Estimation For Rotation Invariant Texture Analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, June2005.
  • Martin Fodslette Moller "A Sacled Conjugate Gradient Algorithm for Fast Supervised Learning", Neural Networks, Vol. 6, pp. 525-533, 1993
  • Rumelhart, D. E. , G. E. Hinton, R. J. Williams,"Learning Internal Representations by Error Propagation, in: Parallel Distributed Processing", Exploration inthe Microstructure of Cognition, Eds. D. E. Rumelhart, J. L. McClelland, MIT Press,Cambridge, MA. , pp 318–362, 1986.
  • Johansson, E. M. , F. U. Dowla, D. M. Goodman, "BackpropagationLearningfor Multi-Layer Feed-Forward Neural Networks Using the Conjugate Gradient Method",Lawrence Livermore National Laboratory, Preprint UCRL-JC-104850, 1990.
  • Battiti, R. , F. Masulli, "BFGS Optimization for Faster and Automated Supervised Learning", INCC 90 Paris, International Neural Network Conference, pp 757–760,1990.
  • Dheeraj S Badde,Anil K Gupta,Vinayak K Patki,"Cascade and Feedforward Neural Network Backpropagation Models for Prediction of Compressive Strength of Ready Mix Concrete", IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) ISSN: 2278-1684, PP: 01-06
  • Meng Joo Er, Shiqian Wu, Juwei Lu, Hock Lye Toh,"Face Recognition with Radial Basis Function Neural networks", IEEE Transactions On Neural Networks, Vol. 13, No. 3, May 2002
  • S Chen,CFN Cowan and PM Grant,"Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks",IEEE Transactions on Neural Networks,Vol. 2,No. 2,March 1991