3D Face Recognitionacross Pose Extremities

  • Parama Bagchi
  • Debotosh Bhattacharjee
  • Mita Nasipuri
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

In this paper, a mathematical model for 3D face image registration has been proposed with poses varying from 0 to ± 90˚ across yaw,pitch and roll. The method which has been proposed in this paper consists of two major steps:- 3D face registration and comparison of the registered image with any neutral frontal posed model in order to measure the accuracy of registration followed by recognition. In this 3D registration and recognition model, a 3D image is transformed from any different pose to frontal pose. After applying the algorithm on the Bosphorus databases, our proposed method registers the images with poses ranging from 0 to 20° with an average rotational error ranging between 0.003 to 0.009. For poses with an orientation of 40° to 45˚, the average rotational error was 0.003 to 0.009 and for poses with 90° the average rotational error was 0.004. Features are extracted from the registered images in the form of face normals. The experimental results which were obtained, on the registered facial images, from the 3D Bosphorus face database, illustrate that our registration scheme has attained a recognition accuracy of 93.33%.

Keywords

Hausdorff’sdistance registration recognition translation scaling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mian, A., Bennamoun, M., Owens, R.: Automatic 3D Detection, Normalization and Recognition. Clarendon, Oxford (1892)Google Scholar
  2. 2.
    Pan, G., Wu, Z., Pan, Y.: Automatic 3D Face Verification From Range Data. In: Proceedings of IEEE Conference on Acoustics, Speech, and Signal Processing, pp. 193–196 (2003)Google Scholar
  3. 3.
    Mian, A.S., Bennamoun, M., Owens, R.: An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(11), 1927–1943 (2007)CrossRefGoogle Scholar
  4. 4.
    Anuar, L.H., Mashohor, S., Mokhtar, M., Adnan, W.A.W.: Nose Tip Region Detection in 3D Facial Model across Large Pose Variation and Facial Expression. International Journal of Computer Science Issues 7(4), 4 (2010)Google Scholar
  5. 5.
    Sangineto, E.: Pose and Expression Independent Facial Landmark Localization Using Dense-SURF and the Hausdorff Distance. IEEE Transactions on Pattern and Machine Intelligence 35(3), 624–638 (2013)CrossRefGoogle Scholar
  6. 6.
    Russ, T.D., Koch, M.W., Little, C.Q.: A 2D Range Hausdorff Approach for 3D Face Recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2005)Google Scholar
  7. 7.
    Aspert, N., Cruz, D., Ebrahimi, T.: Mesh:measuring errors between surfaces using the hausdorff distance. In: IEEE Conference in Multimedia and Expo, pp. 705–708 (2002)Google Scholar
  8. 8.
    Radvar-Esfahlan, H., Tahan, S.: Nonrigid geometric metrology using generalized numerical inspection fixtures. Precision Engineering 36(1), 1–9 (2012)CrossRefGoogle Scholar
  9. 9.
    Alyuz, N., Gokberk, B., Akarun, L.: Adaptive Registration for Registration Based 3D Registration. In: BeFIT 2012 Worksop (2012)Google Scholar
  10. 10.
    Liu, P., Wang, Y., Huang, D., Zhang, Z., Chen, L.: Learning the Spherical Harmonic Features for 3D Face Recognition. IEEE Transactions on Image Processing 22(3), 914–925 (2013)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Dubuisson, M.P., Jain, A.K.: A Modified Hausdorff Distance for Object Matching. In: International Conference on Pattern Recognition, pp. 566–568 (1994)Google Scholar
  12. 12.
    Alyuz, N., Gokberk, B., Akarun, L.: Adaptive Registration for Registration Based 3D Registration. In: BeFIT 2012 Worksop (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Parama Bagchi
    • 1
  • Debotosh Bhattacharjee
    • 2
  • Mita Nasipuri
    • 2
  1. 1.Dept. Of Computer Science & EngineeringMCKV Institute of EngineeringHowrahIndia
  2. 2.Dept. Of Computer Science & EngineeringJadavpur UniversityJadavpurKolkata

Personalised recommendations