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Eye Localization Based on Multi-Channel Correlation Filter Bank

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Pattern Recognition (CCPR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 483))

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Abstract

Accurate eye localization plays a key role in many face analysis related applications. In this paper, we propose a novel eye localization framework with a group of trained filter arrays called multi-channel correlation filter bank (MCCFB). Each filter array in the bank suits to a different face condition, thus combining these filter array can locate eyes more precisely for variable poses, appearances and illuminations when comparing to single filter/filter array. To demonstrate the performance of our strategy, MCCFB is compared to other eye localization methods, experimental results show superiority of our method in detection ratio, localization accuracy and robustness.

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References

  1. Zhou, Z.H., Geng, X.: Projection functions for eye detection. Pattern Recognition 37(5), 1049–1056 (2004)

    Article  MATH  Google Scholar 

  2. Tan, X., Song, F., Zhou, Z., Chen, S.: Enhanced pictorial structures for preciseeye localization under uncontrolled conditions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1621–1628 (2009)

    Google Scholar 

  3. Cao, X., Wei, Y., Wen, F., Sun, J.: Face Alignment by Explicit Shape Regression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2887–2894 (2012)

    Google Scholar 

  4. Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60, 135–164 (2004)

    Article  Google Scholar 

  5. Castrillón-Santana, M., Lorenzo-Navarro, J., Déniz-Suárez, O., Isern-González, J., Falcón-Martel, A.: Multiple face detection at different resolutions for perceptual user interfaces. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 445–452. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)

    Article  Google Scholar 

  7. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)

    Google Scholar 

  8. Bolme, D.S., Draper, B.A., Beveridge, J.R.: Average of synthetic exact filters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2105–2112 (2009)

    Google Scholar 

  9. Bolme, D.S., Beveridge, J.R., Draper, B.A.: Visual object tracking using adaptive correlation filtersc. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)

    Google Scholar 

  10. Heflin, B., Scheirerc, W., Boult, T.E.: For your eyes only. In: IEEE Workshop on the Applications of Computer Vision, pp. 193–200 (2012)

    Google Scholar 

  11. Boddeti, V.N., Kanade, T., Kumar, B.V.: Correlation Filters for Object Alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2291–2298 (2013)

    Google Scholar 

  12. Galoogahi, H.K., Sim, T., Lucey, S.: Multi-channel Correlation Filters. In: IEEE International Conference on Computer Vision, pp. 3072–3079 (2013)

    Google Scholar 

  13. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  14. FGNET Annotation of BioID Dataset, http://www-prima.inrialpes.fr/FGnet/data/11-BioID/bioid_points.html (accessed May 21, 2014)

  15. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report, University of Massachusetts, Amherst (2007)

    Google Scholar 

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Yang, R., Ge, S., Xie, K., Chen, S. (2014). Eye Localization Based on Multi-Channel Correlation Filter Bank. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_33

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  • DOI: https://doi.org/10.1007/978-3-662-45646-0_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45645-3

  • Online ISBN: 978-3-662-45646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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