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Designing Multiple Classifier Systems for Face Recognition

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Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

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Abstract

Face recognition systems often use different images of a subject for training and enrollment. Typically, one may use LDA using all the image samples or train a nearest neighbor classifier for each (separate) set of images. The latter can require that information about lighting or expression about each testing point be available. In this paper, we propose usage of different images in a multiple classifier systems setting. Our main goals are to see (1) what is the preferred use of different images? And (2) can the multiple classifiers generalize well enough across different kinds of images in the testing set, thus mitigating the need of the meta-information? We show that an ensemble of classifiers outperforms the single classifier versions without any tuning, and is as good as a single classifier trained on all the images and tuned on the test set.

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References

  1. Face recognition grand challenge and the biometrics experimentation environment, available at, http://bee-biometrics.org

  2. Flynn, P.J., Bowyer, K.W., Phillips, P.J.: Assessment of time dependency in face recognition: An initial study. In: International Conference on Audio and Video Based Biometric Person Authentication, pp. 44–51 (2003)

    Google Scholar 

  3. Chellappa, R., Wilson, C., Sirohey, S.: Human and machine recognition of faces: A survey. Proceedings of hte IEEE, 83(5), 705–740 (1995)

    Google Scholar 

  4. Samal, A., Iyengar, P.: Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition 25(1), 65–77 (1992)

    Article  Google Scholar 

  5. Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)

    Article  Google Scholar 

  6. Shakhnarovich, G., Moghaddam, G.: Face recognition in subspaces. Handbook of Face Recognition. Springer, Heidelberg (2004)

    Google Scholar 

  7. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)

    MATH  Google Scholar 

  8. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2000)

    Google Scholar 

  9. Martinez, A.M., Kak, A.C.: Pca versus lda. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)

    Article  Google Scholar 

  10. Lu, X., Jain, A.K.: Resampling for face recognition. In: International Conference on Audio and Video Based Biometric Person Authentication, pp. 869–877 (2003)

    Google Scholar 

  11. Beveridge, D., Draper, B.: Evaluation of face recognition algorithms (release version 4.0), available at http://www.cs.colostate.edu/evalfacerec/index.html

  12. Perlibakas, V.: Distance measures for pca-based face recognition. Pattern Recognition Letters 25(6), 711–724 (2004)

    Article  Google Scholar 

  13. Yambor, W., Draper, B., Beveridge, R.: Analyzing PCA-based face recognition algorithms: Eigenvector selection and distance measures (July 2000)

    Google Scholar 

  14. Beveridge, J.R., She, K., Draper, B., Givens, G.: A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 535–542 (2001)

    Google Scholar 

  15. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  16. Wang, X., Tang, X.: Random sampling LDA for face recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 259–265 (2004)

    Google Scholar 

  17. Lemieux, A., Parizeau, M.: Flexible multi-classifier architecture for face recognition systems. Vision Interface (2003)

    Google Scholar 

  18. Chang, K., Bowyer, K.W., Flynn, P.: An evaluation of multi-modal 2d+3d face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

    Google Scholar 

  19. Provost, F., Jensen, D., Oates, T.: Efficient progressive sampling. In: Fifth International of Knowledge Discovery and Databases, pp. 23–32 (1999)

    Google Scholar 

  20. Skurichina, M., Kuncheva, L., Duin, R.P.W.: Bagging and boosting for the nearest mean classifier: Effects of sample size on diversity and accuracy. In: Third International Workshop on Multiple Classifier Systems, pp. 62–71 (2002)

    Google Scholar 

  21. Chawla, N.V., Hall, L.O., Bowyer, K.W., Kegelmeyer, W.P.: Learning ensembles from bites: A scalable and accurate approach. Journal of Machine Learning Research 5, 421–451 (2004)

    MathSciNet  Google Scholar 

  22. GSN Perspectives –Grand challenge sets critical biometric face-off, available at http://www.gsnmagazine.com/dec_04/grand_challenge.html

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, IN, 46556, USA

    Nitesh V. Chawla & Kevin W. Bowyer

Authors
  1. Nitesh V. Chawla
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  2. Kevin W. Bowyer
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, Mail Stop 269-1, 94035-1000, Moffett Field, CA, USA

    Nikunj C. Oza

  2. Signal Processing and Pattern Recognition Laboratory, Electrical and Computer Engineering, Rowan University, 08028, Glassboro, NJ, USA

    Robi Polikar

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, UK

    Josef Kittler

  4. University of Cagliari, Department of Electrical and Electronic Engineering, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2005 Springer-Verlag Berlin Heidelberg

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Chawla, N.V., Bowyer, K.W. (2005). Designing Multiple Classifier Systems for Face Recognition. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_41

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  • DOI: https://doi.org/10.1007/11494683_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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