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Proceedings of Fourth International Conference on Soft Computing for Problem Solving pp 1–12Cite as

Face Recognition Using 2DPCA and ANFIS Classifier

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 336)

Abstract

With the growth of information technology coupled with the need for high security, the application of biometric as identification and recognition process has received special attention. The biometric authentication systems are gaining importance, and in particular, face biometric is more preferred for person authentication because of its easy and non-intrusive method during acquisition procedure. Face recognition is considered to be one of the most reliable biometric, when security issues are taken into concern. Various methods are used for face recognition. To recognize the face, feature extraction becomes a critical problem. In this paper, two-dimensional principle component analysis (2D-PCA) has been applied for feature extraction. The feature vectors are then applied to adaptive neuro-fuzzy inference system (ANFIS) classifier. The result indicates that ANFIS classifier yields 97.1 % of classification accuracy.

Keywords

  • Face recognition
  • 2D-PCA
  • Feature extraction
  • ANFIS

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References

  1. Sirovich, L., Kirby, M.: Low-dimensional procedure for characterization of human faces. J. Optical Soc. Am. 4, 519–524 (1987)

    CrossRef  Google Scholar 

  2. Kirby, M., Sirovich, L.: Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    CrossRef  Google Scholar 

  3. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    CrossRef  Google Scholar 

  4. Pentland, A.: Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 107–119 (2000)

    CrossRef  Google Scholar 

  5. Grudin, M.A.: On internal representations in face recognition systems. Pattern Recogn. 33(7), 1161–1177 (2000)

    CrossRef  Google Scholar 

  6. Valentin, D., Abdi, H., O’Toole, A.J., Cottrell, G.W.: Connectionist models of face processing: a survey. Pattern Recogn. 27(9), 1209–1230 (1994)

    CrossRef  Google Scholar 

  7. Budiharto, W., Rachmawati, R.F.N.: A robust face recognition and tracking system using bayesian filtering and ANFIS controller for service robots. Proc. Eng. 50, 768–777 (2012) (International Conference on Advances Science and Contemporary Engineering)

    Google Scholar 

  8. Khandait, S.P., Thool, R.C., Khandait, P.D.: Comparative analysis of ANFIS and NN approach for expression recognition using geometry method. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(3), 169–174 (2012)

    Google Scholar 

  9. Rajalakshmi, R., Jeyakumar, M.K.: A novel approach to face recognition with pose and illumination variation using support vector machine as classifier. Int. J. Innovative Technol. Exploring Eng. (IJITEE) 3(4), 4–10 (2013)

    Google Scholar 

  10. Fang, Y., Tan, T., Wang, Y.: Fusion of global and local features for face verification. In: Proceedings of 16th International Conference on Pattern Recognition, pp. 382–385 (2002)

    Google Scholar 

  11. Khalid, F., Tengku, T.M., Omar, K.: Face recognition using local geometrical features—PCA with euclidean classifier. In: International Symposium on Information Technology (ITSim 2008), Kuala Lumpur, Malaysia, pp. 1–6, 26–28 Aug 2008

    Google Scholar 

  12. Que, D., Chen, B., Hu, J.: A novel single training sample face recognition algorithm based on modular weighted (2D)2PCA. In: 9th International Conference on Signal Processing (ICSP 2008), Beijing, pp. 1552–1552, 26–29 Oct 2008

    Google Scholar 

  13. Karsili, L., Acan, A.: A radon transform and PCA hybrid for high performance face recognition. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, pp. 246–251, 15–18 Dec 2007

    Google Scholar 

  14. Slavkovic, M., Reljin, B., Gavrovska, A., Milivojevic, M.: Face recognition using Gabor filters, PCA and neural networks. In: 2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP), Bucharest, pp. 35–38, 7–9 July 2013

    Google Scholar 

  15. Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    CrossRef  Google Scholar 

  16. Jang, J.-S.R.: ANFIS: adaptive network based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, G.H. Patel College of Engineering & Technology, Vallabh Vidyanagar, Gujarat, 388120, India

    Hitesh Shah & Rahul Kher

  2. Department of Electronics and Communication Engineering, B & B Institute of Technology, Vallabh Vidyanagar, Gujarat, 388120, India

    Ketan Patel

Authors
  1. Hitesh Shah
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  2. Rahul Kher
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  3. Ketan Patel
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Corresponding author

Correspondence to Hitesh Shah .

Editor information

Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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Shah, H., Kher, R., Patel, K. (2015). Face Recognition Using 2DPCA and ANFIS Classifier. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_1

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  • DOI: https://doi.org/10.1007/978-81-322-2220-0_1

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2219-4

  • Online ISBN: 978-81-322-2220-0

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