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Polymorphous Facial Trait Code

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Computer Vision – ACCV 2009 (ACCV 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5996))

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Abstract

The recently proposed Facial Trait Code (FTC) formulates the component-based face recognition problem as a coding problem using Error-Correcting Code. The development of FTC is based on the extraction of local feature patterns from a large set of faces without significant variations in expression and illumination. This paper reports a new type of FTC that encompasses the faces with large expression variation and under various illumination conditions. We assume that if the patches of a local feature on two different faces look similar in appearance, this pair of patches will also show similar visual patterns when both faces change expressions and are under different illumination conditions. With this assumption, we propose the Polymorphous Facial Trait Code for face recognition under illumination and expression variations. The proposed method outperforms the original Facial Trait Code substantially in solving a strict face verification problem, in which only one facial image per individual is available for enrolling to the gallery set, and the probe set consists of facial images with strong illumination and expression variations.

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References

  1. Liao, R., Li, S.Z.: Face recognition based on multiple facial features. In: Proc. of the 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 239–244. Dekker Inc. (2000)

    Google Scholar 

  2. Ahlberg, J.: Facial feature extraction using deformable graphs and statistical pattern matching. In: Swedish Symposium on Image Analysis, SSAB (1999)

    Google Scholar 

  3. Heisele, B., Thomas, S., Sam, P., Poggio, T.: Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition 36(9), 2007–2017 (2003)

    Article  MATH  Google Scholar 

  4. Heisele, B., Ho, P., Wu, J., Poggio, T.: Face recognition: component-based versus global approaches. CVIU 91(1), 6–12 (2003)

    Google Scholar 

  5. Ivanov, Y., Heisele, B., Serre, T.: Using component features for face recognition. In: FGR 2004, p. 421 (2004)

    Google Scholar 

  6. Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. IJCV 74(2), 167–181 (2007)

    Article  Google Scholar 

  7. Lee, P.H., Hsu, G.S., Chen, T., Hung, Y.P.: Facial trait code and its application to face recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 317–328. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. PAMI 24, 381–396 (2002)

    Google Scholar 

  9. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)

    MATH  Google Scholar 

  10. Martinez, A., Benavente, R.: The ar face database. Technical Report 24, CVC (1998)

    Google Scholar 

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

    Article  Google Scholar 

  12. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. PAMI 19(7), 711–720 (1997)

    Google Scholar 

  13. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. PAMI, 2037–2041 (2006)

    Google Scholar 

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Lee, PH., Hsu, GS., Hung, YP. (2010). Polymorphous Facial Trait Code. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_54

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  • DOI: https://doi.org/10.1007/978-3-642-12297-2_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12296-5

  • Online ISBN: 978-3-642-12297-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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