Advertisement

Automatic Eyes and Nose Detection Using Curvature Analysis

  • J. Matías Di MartinoEmail author
  • Alicia Fernández
  • José Ferrari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

In the present work we propose a method for detecting the nose and eyes position when we observe a scene that contains a face. The main goal of the proposed technique is that it capable of bypassing the 3D explicit mapping of the face and instead take advantage of the information available in the Depth gradient map of the face. To this end we will introduce a simple false positive rejection approach restricting the distance between the eyes, and between the eyes and the nose. The main idea is to use nose candidates to estimate those regions where is expected to find the eyes, and vice versa. Experiments with Texas database are presented and the proposed approach is testes when data presents different power of noise and when faces are in different positions with respect to the camera.

Keywords

Landmark detection Differential 3d reconstruction Nose tip detection Eyes detection 

References

  1. 1.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007)CrossRefGoogle Scholar
  2. 2.
    Besl, P.J., Jain, R.C.: Invariant surface characteristics for 3D object recognition in range images. Computer Vision, Graphics, and Image Processing 33(1), 33–80 (1986)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)CrossRefGoogle Scholar
  4. 4.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three-Dimensional Face Recognition. International Journal of Computer Vision (IJCV) 64(1), 5–30 (2005)CrossRefGoogle Scholar
  5. 5.
    do Carmo, M.: Differential Geometry of Curves and Surfaces. Pearson Education Canada (1976)Google Scholar
  6. 6.
    Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Transactions on Tattern Analysis and Machine Intelligence 28(10), 1695–1700 (2006)CrossRefGoogle Scholar
  7. 7.
    Colombo, A., Cusano, C., Schettini, R.: 3D face detection using curvature analysis. Pattern Recognition 39(3), 444–455 (2006)CrossRefzbMATHGoogle Scholar
  8. 8.
    Di Martino, J.M., Fernández, A., Ferrari, J.A.: 3D curvature analysis with a novel one-shot technique. IEEE International Conference on Image Processing (ICIP2014) (2014)Google Scholar
  9. 9.
    Di Martino, J.M., Fernández, A., Ferrari, J.A.: One-shot 3D gradient field scanning. Optics and Lasers in Engineering 72, 26–38 (2015)CrossRefGoogle Scholar
  10. 10.
    Faltemier, T.C., Bowyer, K.W., Flynn, P.J., Member, S.: A Region Ensemble for 3-D Face Recognition 3(1), 62–73 (2008)Google Scholar
  11. 11.
    Gordon, G.G.: Face recognition based on depth maps and surface curvature. SPIE 1570, Geometric Methods in Computer Vision, pp. 234–247, Sep 1991Google Scholar
  12. 12.
    Gupta, S., Castleman, K., Markey, M., Bovik, A.: Texas 3d face recognition database. In: 2010 IEEE Southwest Symposium on Image Analysis Interpretation (SSIAI), pp. 97–100, May 2010Google Scholar
  13. 13.
    Gupta, S., Markey, M., Bovik, A.: Anthropometric 3D face recognition. International Journal of Computer Vision 90(3), 331–349 (2010)CrossRefGoogle Scholar
  14. 14.
    Li, X., Da, F.: Efficient 3D face recognition handling facial expression and hair occlusion. Image and Vision Computing 30(9), 668–679 (2012)CrossRefGoogle Scholar
  15. 15.
    Mahoor, M.H., Abdel-Mottaleb, M.: Face recognition based on 3D ridge images obtained from range data. Pattern Recognition 42, 445–451 (2009)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • J. Matías Di Martino
    • 1
    Email author
  • Alicia Fernández
    • 1
  • José Ferrari
    • 1
  1. 1.Facultad de IngenieríaUniversidad de la RepúblicaMontevideoUruguay

Personalised recommendations