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Analysis of Different Neural Network Architectures in Face Recognition System

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Proceedings of the Second International Conference on Computer and Communication Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 380))

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Abstract

Face Recognition is considered to be as one of the finest aspects of Computer Vision, also various Feature Extraction and classification techniques including Neural Network Architectures have made it even more interesting. In this paper, an attempt towards developing a model for better feature representation/extraction and cascading it with neural networks classifier is presented. In order to derive better use of face recognition system for faster and better surveillance, analysis is carried out which provides a greater knowledge on the entire process and clarifies on various parameters effecting the system. Most popular Single-Layer Neural Networks such as generalized regression neural network (GRNN) and probabilistic neural network (PNN) are used with different subspace methods to provide a distinguished analysis. The experimental results in this work have revealed that the combination of subspace method with neural networks has increased the robustness and speed of face recognition system. Performance analysis of the proposed model is carried out by conducting the experiments on three benchmarking databases such as ORl, Yale and Feret.

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References

  1. Kumar, K.: Artificial neural network based face detection using gabor feature extraction. Int. J. Adv. Technol. Eng. Res. (IJATER) 2, 220–225 (2012)

    Google Scholar 

  2. Revathy, N., Guhan, T.: Face recognition system using back propagation artificial neural network. Int. J. Adv. Eng. Technol. (IJAET) 3, 321–324 (2012)

    Google Scholar 

  3. Radha, V., Nallammal, N.: Neural network based face recognition using RBFN classifier. In: Proceedings of the World Congress on Engineering and Computer Science (WCECS), vol. 1 (2011)

    Google Scholar 

  4. Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cognit. Neurosci. 3, 71–86 (1991)

    Google Scholar 

  5. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1991)

    Google Scholar 

  6. Scholkopf, B., Smola, A., Müller, K.R.: Kernel principal component analysis. Adv. Kernel Methods Support Vector Learning, pp 327–352 (1999)

    Google Scholar 

  7. Belhumer, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 711–720 (1997)

    Google Scholar 

  8. Chaudhary, U., Mubarak, C.M., Rehman, A., Riyaz, A., Mazhar, S.: Face recognition using PCA-BPNN algorithm. Int. J. Modren Eng. Res. (IJMER), 2, 1366–1370 (2012)

    Google Scholar 

  9. Daramola, S.A., Odeghe, O.S.: Efficient face recognition system using artificial neural network. Int. J. Comput. Appl. 41(21), 12–15 (2012)

    Google Scholar 

  10. Specht, D.F.: A general regression neural network. IEEE Trans. Neural Networks 2(6), 568–576 (1991)

    Article  Google Scholar 

  11. Demuth, H.B., Beale, M.: Neural network toolbox for use with MATLAB Users Guide Version 4. Mathworks (2002)

    Google Scholar 

  12. Specht, D.F.: Probabilistic neural networks. IEEE Int. Conf. Neural Networks, pp. 525–532 (1990)

    Google Scholar 

  13. Beale, H., Hagan, M.T., Demuth, H.B.: Neural network toolbox users guide 2013a, Mathworks (2013)

    Google Scholar 

  14. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  15. http://www.nist.gov/itl/iad/ig/colorferet.cfm

  16. http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  17. Sahoolizadeh, A.H., Heidari, B.Z., Dehghani, C.H.: A new face recognition method using PCA, LDA and neural network. Int. J. Electr. Electron. Eng. 2(5), 6–12 (2008)

    Google Scholar 

  18. Esbati, H., Shirazi, J.: Face recognition with PCA and KPCA using Elman neural network and SVM. Int. J. Electr. Electron. Eng. 5(10), 135–140 (2011)

    Google Scholar 

  19. Hoang, L.T.: Applying artificial neural networks for face recognition. In: Advances in Artificial Neural System, vol. 2011, Hindawi Publishing Corp

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of CS, Jain University, Bangalore, India

    E. V. Sudhanva

  2. Department of MCA, Sri Jayachamarajendra College of Engineering, Mysore, India

    V. N. Manjunath Aradhya

  3. Department of CSE, HKBK College of Engineering, Bangalore, India

    C. Naveena

Authors
  1. E. V. Sudhanva
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  2. V. N. Manjunath Aradhya
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  3. C. Naveena
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Corresponding author

Correspondence to E. V. Sudhanva .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Sudhanva, E.V., Manjunath Aradhya, V.N., Naveena, C. (2016). Analysis of Different Neural Network Architectures in Face Recognition System. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_45

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  • DOI: https://doi.org/10.1007/978-81-322-2523-2_45

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2522-5

  • Online ISBN: 978-81-322-2523-2

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