Facial Expression Recognition Using Extended Local Binary Patterns of 3D Curvature

  • Soon-Yong Chun
  • Chan-Su Lee
  • Sang-Heon Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 240)


This paper presents extended local binary patterns (LBP) for facial expression analysis from 3D depth map images. Recognition of facial expressions is important to understand human emotion and develop affective human computer interaction. LBP and its extensions are frequently used for texture classification and face identification and detection. In the 3D surface analysis, curvature is very important characteristics. This paper presents an extension of LBP for modeling curvature from 3D depth map images. The extended curvature LBP (CLBP) is used for facial expression recognition. Experimental results using Bosphorus facial expression database show better performance by 3D curvature and the combination of 3D curvature and 2D images than by conventional 2D or 2D + 3D approaches.



This work was supported by the DGIST R&D Program of the Ministry of Education, Science and Technology of Korea (13-IT-03).


  1. 1.
    Ojala T, Pietikenen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distribution. Pattern Recognit 29Google Scholar
  2. 2.
    Pietikainen M, Hadid A, Zhao G, Ahonen T (2011) In computer vision using local binary patterns. Springer (2011)Google Scholar
  3. 3.
    Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with local binary patterns. In: Proceedings of ECCV, pp 469–481Google Scholar
  4. 4.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans PAMI 28Google Scholar
  5. 5.
    Vu NS, Caplier A (2011) Enhanced patterns of oriented edge magnitudes for face recognition and image matching. IEEE Trans Image Process 21:135–1365MathSciNetGoogle Scholar
  6. 6.
    Shan G, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816Google Scholar
  7. 7.
    Sha T, Song M, Bu J, Chen C, Tao D (2011) Feature level analysis for 3D facial expression recognition. Neurocomputing 74:2135–2141CrossRefGoogle Scholar
  8. 8.
    Maalej A, Amor BB, Daoudi M, Srivastava A, Berretti S (2011) Shape analysis of local facial patches for 3D facial expression recognition. Pattern Recognit 44:1581–1589CrossRefGoogle Scholar
  9. 9.
    Wang J, Yin L, Wei X, Sun Y (2006) 3D facial expression recognition based on primitive surface feature distribution. In: IEEE Conference on computer vision and pattern recognition, pp 1399–1406Google Scholar
  10. 10.
    Savran A, Alyuz N, Dibeklioglu H, Gokberk O, Sankur B, Akarun I (2008) Bosphorus database for 3D face analysis. In: Proceedings of the Workshop on BIOIDGoogle Scholar
  11. 11.
    Zhang B, Gao Y (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19:533–544CrossRefMathSciNetGoogle Scholar
  12. 12.
    Kalra P, Peleg S (2006) Description of interest regions with center-symmetric local binary patterns. In: Proceedings of the ICVGIP, LNCS 4338, pp 58–69Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht(Outside the USA) 2013

Authors and Affiliations

  • Soon-Yong Chun
    • 1
  • Chan-Su Lee
    • 1
  • Sang-Heon Lee
    • 2
  1. 1.Department of Electronic EngineeringYeungnam UniversityGyeongsangbook-doKorea
  2. 2.Daegu Gyeongbuk Institute of Science and TechnologyDaeguKorea

Personalised recommendations