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Head Pose Estimation Relying on Appearance-Based Nose Region Analysis

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Computer Vision and Graphics (ICCVG 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8671))

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Abstract

In this paper we explore the possibilities of recognizing head orientation based on the appearance of the nose. We demonstrate that the features extracted from that region possess high discriminating power with regards to the head orientation. Extensive experimental validation study, performed using the benchmark data, confirmed high effectiveness of the proposed approach compared with the baseline techniques that rely on the analysis of the entire facial region.

This work has been supported by the European Regional Development Fund under Operational Programme Innovative Economy 2007-2013, based on the Agreement No. UDA-POIG.01.04.00-24-138/11-01.

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References

  1. Balasubramanian, V.N., Ye, J., Panchanathan, S.: Biased manifold embedding: A framework for person-independent head pose estimation. In: Proc. IEEE CVPR, pp. 1–7 (2007)

    Google Scholar 

  2. Chen, L., Zhang, L., Hu, Y., Li, M., Zhang, H.: Head pose estimation using Fisher Manifold learning. In: Proc. IEEE AMFG, pp. 203–207 (2003)

    Google Scholar 

  3. Dornaika, F., Davoine, F.: Head and facial animation tracking using appearance-adaptive models and particle filters. In: Proc. IEEE CVPR, pp. 153–153 (2004)

    Google Scholar 

  4. Fanelli, G., Gall, J., Van Gool, L.: Real time head pose estimation with random regression forests. In: Proc. IEEE CVPR, pp. 617–624 (2011)

    Google Scholar 

  5. Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial structures. In: Proc. Pointing 2004, ICPR, Int. Workshop on Visual Observation of Deictic Gestures, pp. 1–9 (2004)

    Google Scholar 

  6. Gourier, N., Maisonnasse, J., Hall, D., Crowley, J.L.: Head pose estimation on low resolution images. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 270–280. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Hachaj, T., Ogiela, M.R., Piekarczyk, M.: Real-time recognition of selected karate techniques using GDL approach. In: Choras, R.S. (ed.) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol. 233, pp. 97–104. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  8. Hansen, D.W., Ji, Q.: In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32, 478–500 (2010)

    Article  Google Scholar 

  9. Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96 3 14 (2003)

    Google Scholar 

  10. Kawulok, M., Szymanek, J.: Precise multi-level face detector for advanced analysis of facial images. Image Process., IET 6(2), 95–103 (2012)

    Article  MathSciNet  Google Scholar 

  11. Kawulok, M., Nalepa, J.: Support vector machines training data selection using a genetic algorithm. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR&SPR 2012. LNCS, vol. 7626, pp. 557–565. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Krüger, V., Sommer, G.: Gabor wavelet networks for efficient head pose estimation. Image and Vision Comput 20(9), 665–672 (2002)

    Article  Google Scholar 

  13. Ma, B., Chai, X., Wang, T.: A novel feature descriptor based on biologically inspired feature for head pose estimation. Neurocomputing 115, 1–10 (2013)

    Article  Google Scholar 

  14. McKenna, S.J., Gong, S.: Real-time face pose estimation. Real-Time Imaging 4(5), 333–347 (1998)

    Article  Google Scholar 

  15. Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 607–626 (2009)

    Article  Google Scholar 

  16. Nalepa, J., Kawulok, M.: Adaptive genetic algorithm to select training data for support vector machines. In: EvoApplications 2014. EvoIASP. LNCS, Springer (in press 2014)

    Google Scholar 

  17. Niyogi, S., Freeman, W.T.: Example-based head tracking. In: Proc. IEEE FG, pp. 374–378 (1996)

    Google Scholar 

  18. Orozco, J., Gong, S., Xiang, T.: Head pose classification in crowded scenes. In: Proc. BMVC., vol. 1, p. 120 (2009)

    Google Scholar 

  19. Sherrah, J., Gong, S., Ong, E.J.: Understanding pose discrimination in similarity space. In: Proc. BMVC, pp. 1–10 (1999)

    Google Scholar 

  20. Starosolski, R.: New simple and efficient color space transformations for lossless image compression. J. of Vis. Commun. and Image Represent. 25(5), 1056–1063 (2014)

    Article  Google Scholar 

  21. Stiefelhagen, R.: Estimating head pose with neural networks. In: Proc. Pointing 2004 ICPR Workshop (2004)

    Google Scholar 

  22. Storer, M., Urschler, M., Bischof, H.: 3D-MAM: 3D morphable appearance model for efficient fine head pose estimation from still images. In: Proc. IEEE ICCV Workshops, pp. 192–199 (2009)

    Google Scholar 

  23. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. (1), pp. 511–518 (2001)

    Google Scholar 

  24. Voit, M., Nickel, K., Stiefelhagen, R.: Neural network-based head pose estimation and multi-view fusion. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 291–298. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  25. Zhang, Z., Hu, Y., Liu, M., Huang, T.: Head pose estimation in seminar room using multi view face detectors. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 299–304. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  26. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proc. IEEE CVPR, pp. 2879–2886 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Future Processing, Bojkowska 37A, 44-100, Gliwice, Poland

    Krzysztof Pawelczyk & Michał Kawulok

  2. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Michał Kawulok

Authors
  1. Krzysztof Pawelczyk
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  2. Michał Kawulok
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Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences (SGGW), Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences - SGGW, Nowoursynowska str. 159, 02-776, Warsaw, Poland

    Ryszard Kozera

  3. The .enpeda.. Project, The University of Auckland, New Zealand

    Bok-Suk Shin

  4. Institute of Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Konrad Wojciechowski

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© 2014 Springer International Publishing Switzerland

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Pawelczyk, K., Kawulok, M. (2014). Head Pose Estimation Relying on Appearance-Based Nose Region Analysis. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_61

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  • DOI: https://doi.org/10.1007/978-3-319-11331-9_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11330-2

  • Online ISBN: 978-3-319-11331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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