3D Facial Expression Classification Using 3D Facial Surface Normals

  • Hamimah UjirEmail author
  • Michael Spann
  • Irwandi Hipni Mohamad Hipiny
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)


With current advanced 3D scanners technology, direct anthropometric measurements are easily obtainable and it offers 3D geometrical data suitable for 3D face processing studies. Instead of using the raw 3D facial points, we extracted one of its derivatives, 3D facial surface normals. We constructed a statistical model for variations in facial shape due to changes in six basic expressions using 3D facial surface normals as the feature vectors. In particular, we are interested in how such facial expression variations manifest themselves in terms of changes in the field of 3D facial surface normals. Using our approach, using 3D facial surface normal yields a better performance than 3D facial points and 3D distance measurements in facial expression classification. The attained results suggest surface normals do indeed produce a comparable result particularly for six basic facial expressions with no intensity information.


Facial expression classification Principal component analysis 3D facial features 



This preliminary work was supported by Malaysian Ministry of Higher Education through Exploratory Research Grant Scheme 1020/2013(17) to Dr Hamimah Ujir. Dr Hamimah Ujir is a scholar at the Faculty of Computer Science and Information Technology, UNIMAS where the work was carried out.


  1. 1.
    Sandbach G, Zafeiriou S, Pantic M, Rueckert D (2012) Recognition of 3D facial expression dynamics. J Image Vis Comput (in press)Google Scholar
  2. 2.
    Tang H, Huang TS (2008) 3D facial expression recognition based on properties of line segments connecting facial feature points. In: 8th IEEE international conference on automatic face & gesture recognition, pp 1–6Google Scholar
  3. 3.
    Gupta S, Markey MK, Bovik AC (2010) Anthropometric 3D face recognition. Int J Comput Vis 90(3):341–349 (Springer)CrossRefGoogle Scholar
  4. 4.
    Vezzetti E, Marcolin F (2012) 3D human face description: landmarks measures and geometrical features. J Image Vis Comput 30(10):698–712CrossRefGoogle Scholar
  5. 5.
    Gökberk B, İrfanoğlu MO, Akarun L (2006) 3D shape-based face representation and feature extraction for face recognition. J Image Vis Comput 24(8):857–869CrossRefGoogle Scholar
  6. 6.
    Soyel H, Demirel H (2007) Facial expression recognition using 3D facial feature distances. LNCS book series. Image and analysis recognition. Springer, Berlin, pp 831–838Google Scholar
  7. 7.
    Wang J, Yin L, Wei X, Sun Y (2006) 3D facial expression based on primitive surface feature distribution. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1399–1406Google Scholar
  8. 8.
    Maalej A, Ben Amor B, Daoudi M, Srivastava A, Berreti S (2010) Local 3D shape analysis for 3D facial expression recognition. In: International conference on pattern recognition, pp 4129–4132Google Scholar
  9. 9.
    Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Lale A (2008) Bosphorus database for 3D face analysis. In: Schouten B, Juul NC, Drygajlo A, Tistarelli M (eds) Biometrics and identity management. Springer, Berlin, Heidelberg, pp 47–56Google Scholar
  10. 10.
    Carmo MD (1976) Differential geometry of curves and surfaces. Prentice Hall, Englewood CliffsGoogle Scholar
  11. 11.
    Ceolin SR (2012) Facial shape space using statistical models from surface normal. Ph.D., University of YorkGoogle Scholar
  12. 12.
    Ghahramani Z (2004) Unsupervised learning. In: Bousquet O, Raetsch G, von Luxburg U (eds) Advanced lectures on machine learning. Springer, BerlinGoogle Scholar
  13. 13.
    Gong B, Wang Y, Liu J, Tang X (2009) Automatic facial expression recognition on a single 3D face by exploring shape deformation. ACM Multimedia, pp 569–572Google Scholar
  14. 14.
    Yin L, Wei X, Sun Y, Wang J, Rosato MJ (2006) A 3D facial expression database for facial behavior research. In: 7th international conference on automatic face and gesture recognition (FGR06), pp 211–216Google Scholar
  15. 15.
    Sandbach G, Zafeiriou S, Pantic M (2012) Local normal binary patterns for 3D facial action unit detection. In: Proceedings of the IEEE international conference on image processing (ICIP 2012), Orlando, FL, USA, Oct 2012, pp 1813–1816Google Scholar
  16. 16.
    Mpiperis I, Malassiotis S, Strintzis MG (2008) Bilinear models for 3-D face and facial expression recognition. IEEE Trans Inf Forensics Secur 3(3):498–511CrossRefGoogle Scholar
  17. 17.
    Pinto SCD, Mena-Chalco JP, Lopes FM, Velho L, Cesar RM (2011) 3D facial expression analysis by using 2D and 3D wavelet transforms. In: 18th IEEE international conference on image processing (ICIP), pp 1281–1284Google Scholar
  18. 18.
    Berretti S, Ben Amor B, Daoudi M, Del Bimbo A (2011) 3D facial expression recognition using SIFT descriptors of automatically detected keypoints. Vis Comput 27(11):1021–1036CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Hamimah Ujir
    • 1
    Email author
  • Michael Spann
    • 2
  • Irwandi Hipni Mohamad Hipiny
    • 3
  1. 1.Department of Computational Science and Mathematics, Faculty of Computer Science and Information TechnologyUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.School of Electronic, Electrical and Computer EngineeringUniversity of BirminghamBirminghamUK
  3. 3.Department of Computing and Software Engineering, Faculty of Computer Science and Information TechnologyUniversiti Malaysia SarawakKota SamarahanMalaysia

Personalised recommendations