Naouar Belghini; Arsalane Zarghili; Jamal Kharroubi; Aicha Majda

Learning a Backpropagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition

Full Text (PDF, 1109KB), PP.8-14

Views: 0 Downloads: 0

Author(s)

Naouar Belghini ^1,* Arsalane Zarghili ¹ Jamal Kharroubi ¹ Aicha Majda ¹

1. Sidi Mohamed Ben Abdellah University, FSTF, Morocco

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2012.08.02

Received: 11 Apr. 2012 / Revised: 22 May 2012 / Accepted: 21 Jun. 2012 / Published: 8 Aug. 2012

Index Terms

Back propagation, Neural Network, Face recognition, Error function, Bhattacharyya distance

Abstract

In this paper, a color face recognition system is developed to identify human faces using Back propagation neural network. The architecture we adopt is All-Class-in-One-Network, where all the classes are placed in a single network. To accelerate the learning process we propose the use of Bhattacharyya distance as total error to train the network. In the experimental section we compare how the algorithm converge using the mean square error and the Bhattacharyya distance. Experimental results indicated that the image faces can be recognized by the proposed system effectively and swiftly.

Cite This Paper

Naouar Belghini, Arsalane Zarghili, Jamal Kharroubi, Aicha Majda,"Learning a Backpropagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition", IJIGSP, vol.4, no.8, pp.8-14, 2012. DOI: 10.5815/ijigsp.2012.08.02

Reference

[1]Dr Libor Spacek, Faces Directories, http://cswww.essex.ac.uk/mv/allfaces.

[2]Georgia Database, http://www.anefian.com/research/face_reco.htm

[3]S.M. Shamsuddin, R. Alwee, P. Kuppusamy, M. Darus, Study of cost functions in Three Term Back propagation for classification problems. In Nature & Biologically Inspired Computing. 2009

[4]W. Zheng, X. Zho, C. Zou, L. Zhao, Facial Expression Recognition Using Kernel Canonical Correlation Analysis. IEEE Transactions on Neural Networks. Vol. 17, No. 1, 2006, pp. 233—238.

[5]Y. Liu, Y. Chen, Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks, Vol. 18, 2007, pp. 178-192

[6]Y.Khalid and Y.Peng, A Novel Approach to Using Color Information in Improving Face Recognition Systems Based on Multi-Layer Neural Networks. Recent Advances in Face Recognition, USA. 2008

[7]T. Falas and A-G. Stafylopatis, The Impact of The Error Function Selection in Neural Network-based Classifiers. International Joint Conference on Neural Network. 1999

[8]Sung-Hyuk Cha, Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions. International Journal of Mathematical Models and Methods in Applied Sciences. 2007

[9]N. A. Thacker, F. J. Aherne and P. I. Rockett, The Bhattacharyya Metric as an Absolute Similarity Measure for Frequency Coded Data. publiseed in Kybernetika, 1997, pp. 363-368

[10]E.Choi and C. Lee, Feature extraction based on the Bhattacharyya distance, Pattern Recognition, pp. 1703 – 1709, 2003.

[11]Euisun Choi and Chulhee Lee, Estimation of classification error Based on the Bhattacharyya distance for Multimodal Data. Geoscience and Remote Sensing Symposium. 2001

[12]Thomas S. Huang, Ziyou Xiong, ZhenQiu Zhang, Face Recognition Applications. Handbook of Face Recognition, 2011, pp. 617-638

[13]T.J. Stonham, Practical face recognition and verification with WISARD, Aspects of Face Processing, 1986, pp. 426-441

[14]N. Belghini, A. Zarghili, J. Kharroubi and A. Majda, Sparse Random Projection and Dimensionality Reduction Applied on Face Recognition, Proceedings of International Conference on Intelligent Systems & Data Processing, Gujarat, India, pp. 78-82, January 2011.

[15]D. Bhattacharjee, D. K. Basu, M. Nasipuri, and M. Kundu, Human face recognition using fuzzy multilayer perceptron. Soft Computing – A Fusion of Foundations, Methodologies and Applications, pp. 559–570, April 2009.

[16]V. Radha, and N. Nallammal, Neural Network Based Face Recognition Using RBFN Classifier, Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, 2011.

International Journal of Image, Graphics and Signal Processing (IJIGSP)