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International Workshop on Cognitive Vision

ICVW 2008: Cognitive Vision pp 1–13Cite as

Face Recognition with Biologically Motivated Boosted Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5329))

Abstract

The current work presents a new face recognition algorithm based on novel biologically-motivated image features and a new learning algorithm, the Pseudo Quadratic Discriminant Classifier (PQDC). The recognition approach consists of construction of a face similarity function, which is the result of combining linear projections of the image features. In order to combine this multitude of features the AdaBoost technique is applied. The multi-category face recognition problem is reformulated as a binary classification task to enable proper boosting. The proposed recognition technique, using the Pseudo Quadratic Discriminant Classifier, successfully boosted the image features. Its performance was better than the performance of the Grayscale Eigenface and L,a,b Eigenface algorithms.

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References

  1. Kanade, T.: Computer Recognition of Human Faces. Birkhäuser Verlag, Stuttgart (1977)

    Book  Google Scholar 

  2. Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W.: Overview of the Face Recognition Grand Challenge. In: IEEE CVPR 2005, vol. 1, pp. 947–954 (2005)

    Google Scholar 

  3. Turk, M.A., Pentland, A.P.: Face Recognition Using Eigenfaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)

    Google Scholar 

  4. Moghaddam, B., Pentland, A.: Beyond Euclidean Eigenspaces: Bayesian Matching for Visual Recognition, Mitsubishi Electric Research Laboratories (1998)

    Google Scholar 

  5. Savvides, M., Kumar, B.V.K.V., Khosla, P.K.: Eigenphases vs Eigenfaces. In: Proceedings of ICPR 2004, August 23–26, vol. 3, pp. 810–813 (2004)

    Google Scholar 

  6. Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. J. Optical Soc. Amer. 2(7), 1,160–1,169 (1985)

    Article  Google Scholar 

  7. Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.v.d.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. On PAMI 19(7), 775–779 (1997)

    Article  Google Scholar 

  8. Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)

    Article  Google Scholar 

  9. Shan, S., Yang, P., Chen, X., Gao, W.: AdaBoost Gabor Fisher Classifier for Face Recognition. In: Zhao, W., Gong, S., Tang, X. (eds.) AMFG 2005. LNCS, vol. 3723, pp. 279–292. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Schapire, R.E.: The boosting approach to machine learning: An overview. In: Denison, D.D., Hansen, M.H., Holmes, C., Mallick, B., Yu, B. (eds.) Nonlinear Estimation and Classification. Springer, Heidelberg (2003)

    Google Scholar 

  11. HunterLab App. Note: Hunter L, a, b Color Scale, August 1-15, Vol. 8(9) (1996)

    Google Scholar 

  12. Wyszecki, G., Stiles, W.S.: Color Science - Concepts and Methods, Quantitative Data and Formulae, 2nd edn. Wiley-Interscience, New York (2000)

    Google Scholar 

  13. Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. Patt. Anal. Mach. Intell. 20(11) (November 1998)

    Google Scholar 

  14. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons Inc., Chichester (2001)

    MATH  Google Scholar 

  15. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J 16(5), 295–306 (1998)

    Article  Google Scholar 

  16. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)

    Article  Google Scholar 

  17. Beveridge, R., Bolme, D., Teixeira, M., Draper, B.: The CSU face identification evaluation system user’s guide: Version 5, Tech. Rep., CSU (May 2003)

    Google Scholar 

  18. Schölkopf, B., Smola, A., Müller, K.-R.: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10(5), 1299–1319 (1998)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Technion – Israel Institute of Technology, Haifa, 32000, Israel

    Erez Berkovich & Moshe Gur

  2. Evoked Potentials Laboratory, Technion – Israel Institute of Technology, Haifa, 32000, Israel

    Hillel Pratt

Authors
  1. Erez Berkovich
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  2. Hillel Pratt
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  3. Moshe Gur
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Editor information

Editors and Affiliations

  1. IDIAP Research Institute, Centre Du Parc, Rue Marconi 19, 1920, Martigny, Switzerland

    Barbara Caputo

  2. Automation and Control Institute, Vienna University of Technology, Gusshausstr, 27/376, 1040, Vienna, Austria

    Markus Vincze

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© 2008 Springer-Verlag Berlin Heidelberg

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Berkovich, E., Pratt, H., Gur, M. (2008). Face Recognition with Biologically Motivated Boosted Features. In: Caputo, B., Vincze, M. (eds) Cognitive Vision. ICVW 2008. Lecture Notes in Computer Science, vol 5329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92781-5_1

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  • DOI: https://doi.org/10.1007/978-3-540-92781-5_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92780-8

  • Online ISBN: 978-3-540-92781-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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