Block-Based Search Space Reduction Technique for Face Detection Using Shoulder and Head Curves

  • Supriya Sathyanarayana
  • Ravi Kumar Satzoda
  • Suchitra Sathyanarayana
  • Srikanthan Thambipillai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

Conventional face detection techniques usually employ sliding window based approaches involving series of classifiers to accurately determine the position of the face in an input image resulting in high computational redundancy. Pre-processing techniques are being investigated to reduce the search space for face detection. In this paper, we propose a systematic approach to reduce the search space for face detection using head and shoulder curves. The proposed method includes Gradient Angle Histograms (GAH) that are applied in a block-based manner to detect these curves, which are further associated to determine the search space for face detection. A performance evaluation of the proposed method on the datasets (CASIA and Buffy) shows that an average search space reduction upto 80% is achieved with detection rates of over 90% for specific parameters of the dataset.

Keywords

search space reduction face detection visual search face localization computational efficiency head and shoulder curve 

References

  1. 1.
    Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Technical Report Microsoft Research (2010)Google Scholar
  2. 2.
    Viola, P., Jones, M.: Robust real-time face detection. In: IEEE ICCV, vol. 2, pp. 747–747 (2001)Google Scholar
  3. 3.
    Sznitman, R., Jedynak, B.: Active testing for face detection and localization. IEEE Trans. PAMI 32(10), 1914–1920 (2010)CrossRefGoogle Scholar
  4. 4.
    Xu, D., Chen, Y.L., Wu, X., Ou, Y., Xu, Y.: Integrated approach of skin-color detection and depth information for hand and face localization. In: 2011 IEEE Intl. Conf. on Robotics and Biomimetics (ROBIO), pp. 952–956 (2011)CrossRefGoogle Scholar
  5. 5.
    He, F., Li, Y., Wang, S., Ding, X.: A novel hierarchical framework for human head-shoulder detection. In: 4th Intl. Cong. on Img. & Sig. Proc., vol. 3, pp. 1485–1489. IEEE (2011)Google Scholar
  6. 6.
    Sun, Y., Wang, Y., He, Y., Hua, Y.: Head-and-shoulder detection in varying pose. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 12–20. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Satzoda, R.K., Suchitra, S., Srikanthan, T.: Gradient angle histograms for efficient linear hough transform. In: 16th IEEE Intl. Conf. on Img. Proc. (ICIP), pp. 3273–3276 (2009)Google Scholar
  8. 8.
    Kolesnikov, A.: Constrained piecewise linear approximation of digital curves. In: 19th Intl. Conf. Pat. Rec (ICPR), pp. 1–4. IEEE (2008)Google Scholar
  9. 9.
    Zucker, S.W., David, C., Dobbins, A., Iverson, L.: The organization of curve detection: Coarse tangent fields and fine spline coverings. In: 2nd Intl. Conf. on Comp. Vis., pp. 568–577 (1988)Google Scholar
  10. 10.
    Gonzalez, R.C., Woods, R.E.: Digital image processing, vol. 2 (2009)Google Scholar
  11. 11.
    Ferrari, V., Eichner, M., Marin-Jimenez, M., Zisserman, A.: Buffy stickmen v 3.01 datasetGoogle Scholar
  12. 12.
    Chinese Academy of Sciences Institute of Automation, C.A.: CASIA-FaceV5 datasetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Supriya Sathyanarayana
    • 1
  • Ravi Kumar Satzoda
    • 1
  • Suchitra Sathyanarayana
    • 1
  • Srikanthan Thambipillai
    • 1
  1. 1.Nanyang Technological UniversitySingapore

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