Face Recognition Using Eigenfaces

  • G. Md. Zafaruddin
  • H. S. FadewarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


In this paper, we propose a PCA-based face recognition system implemented using the concept of neural networks. This system has three stages, viz. pre processing, PCA and face recognition. The first stage, preprocessing performs head orientation and normalization. The aspects that matter for the identification process are ploughed out using Principal Component Analysis (PCA). Using the initial set of facial images, we calculate the corresponding eigenfaces. Every new face is presented into the face space and is characterized by weighted-sum of corresponding eigenfaces that is used to recognize a face. To implement this face recognition system, we have created a database of faces with the help of neural networks and we have built one separate network per person. We obtain a descriptor by projecting a face as input on the eigenface space, then that descriptor is fed as input to the pre-trained network of each object. We select and report that which has the max output provided it passes the threshold already defined for the recognition system. Testing of the algorithm is done on ORL Database.


PCA (Principal component Analysis) Neural network Eigenface Eigenvector 


  1. 1.
    Rathi, R., Chaudhary, M., Chandra, B.: An application of face recognition system using image processing and neural networks. Int. J. Comput. Technol. Appl. 3(1), Jan-Feb 2012Google Scholar
  2. 2.
    Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), June 2009CrossRefGoogle Scholar
  3. 3.
    Sung, K., Poggio, T.: Example-based Learning for View-based Human Face Detection. A.I. Memo 1521, CBCL Paper 112, MIT, Dec 1994Google Scholar
  4. 4.
    Sellahewa, H., Jassim, S.A.: Image quality-based adaptive face recognition. In: IEEE Transactions on Instrumentation and Measurement and Measurement, pp. 805–813. IEEE (2010)Google Scholar
  5. 5.
    Patil, M., Iyer, B., Arya, R.: Performance evaluation of PCA and ICA algorithm for facial expression recognition application. In: Proceedings of Fifth International Conference on Soft Computing for Problem Solving, pp. 965–976 (2016)Google Scholar
  6. 6.
    Shermina, J.: Face Recognition System using Multi Linear Principal Component Analysis and Locality Preserving Projection. In: IEEE GCC Conference and Exhibition, 19–22 Feb, pp. 283–286. Stirling, UK (2011)Google Scholar
  7. 7.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. 4, 519–524 (1987)CrossRefGoogle Scholar
  8. 8.
    Lone, M.A., Zakariya, S.M., Ali, R.: Automatic face recognition system by combining four individual algorithms. In: International Conference on Computational Intelligence and Communication Systems IEEE, pp. 222–226 (2011)Google Scholar
  9. 9.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  10. 10.
    Dimitri: Eigenface-Based Facial Recognition (2013). Accessed 13 Feb 2003
  11. 11.
    Rowley, H.A., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–30 (1998)CrossRefGoogle Scholar
  12. 12.
    Fraud, R., et al.: A fast and accurate face detector based on neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 23(1), 42–53 (2001)Google Scholar
  13. 13.
    Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural network approach. In: IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition, pp. 1–24 (1997)CrossRefGoogle Scholar
  14. 14.
    Galbally, J., McCool, C., Fierrez, J., Marcel, S., Ortega-Garcia, J.: On the vulnerability of face verification systems to hill-climbing attacks. Pattern Recogn. 43(3), 1027–1038 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Millennium Institute of ManagementAurangabadIndia
  2. 2.Department of Computational SciencesSRTMUNandedIndia

Personalised recommendations