Advertisement

Utilizing Deep Learning and 3DLBP for 3D Face Recognition

  • João Baptista Cardia Neto
  • Aparecido Nilceu Marana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

Methods based on biometrics can help prevent frauds and do personal identification in day-to-day activities. Automated Face Recognition is one of the most popular research subjects since it has several important properties, such as universality, acceptability, low costs, and covert identification. In constrained environments methods based on 2D features can outperform the human capacity for face recognition but, once occlusion and other types of challenges are presented, the aforementioned methods do not perform so well. To deal with such problems 3D data and deep learning based methods can be a solution. In this paper we propose the utilization of Convolutional Neural Networks (CNN) with low-level 3D local features (3DLBP) for face recognition. The 3D local features are extracted from depth maps captured by a Kinect sensor. Experimental results on Eurecom database show that this proposal is promising, since, in average, almost 90% of the faces were correctly recognized.

Keywords

Biometrics 3D face recognition 3D local features Depth maps Kinect Deep learning Convolutional Neural Networks 

References

  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992).  https://doi.org/10.1109/34.121791 CrossRefGoogle Scholar
  2. 2.
    Bolle, R., Pankanti, S.: Biometrics. Personal Identification in Networked Society. Kluwer Academic Publishers, Norwell (1998)Google Scholar
  3. 3.
    Cardia Neto, J.B., Marana, A.N.: 3DLBP and HAOG fusion for face recognition utilizing kinect as a 3D scanner. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 66–73. ACM (2015)Google Scholar
  4. 4.
    Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image Vis. Comput. 23(3), 299–309 (2005). http://www.sciencedirect.com/science/article/pii/S0262885604001179 CrossRefGoogle Scholar
  5. 5.
    Drira, H., Amor, B.B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)CrossRefGoogle Scholar
  6. 6.
    Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: Proceedings of the British Machine Vision Conference, pp. 90.1–90.10. BMVA Press (2006).  https://doi.org/10.5244/C.20.90
  7. 7.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  8. 8.
    Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, M.N., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–649 (2007).  https://doi.org/10.1109/TPAMI.2007.1017 CrossRefGoogle Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  10. 10.
    Lawrence, S., Giles, C., Tsoi, A.C., Back, A.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997). http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=554195&tag=1
  11. 11.
    LeCun, Y., Bengio, Y.: The handbook of brain theory and neural networks. In: Convolutional Networks for Images, Speech, and Time Series, pp. 255–258. MIT Press, Cambridge, MA, USA (1998). http://dl.acm.org/citation.cfm?id=303568.303704
  12. 12.
    LeCun, Y., Kavukvuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of International Symposium on Circuits and Systems (ISCAS 2010). IEEE (2010)Google Scholar
  13. 13.
    Li, B., Mian, A., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 186–192 (2013)Google Scholar
  14. 14.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer, London (2011).  https://doi.org/10.1007/978-0-85729-932-1. http://dblp.uni-trier.de/db/books/daglib/0027896.html MATHGoogle Scholar
  15. 15.
    Min, R., Kose, N., Dugelay, J.L.: Kinectfacedb: a kinect database for face recognition. IEEE Trans. Syst. Man Cybern. Syst. 44(11), 1534–1548 (2014)CrossRefGoogle Scholar
  16. 16.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  17. 17.
    Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC. vol. 1, p. 6 (2015)Google Scholar
  18. 18.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes 3rd Edition: The Art of Scientific Computing, 3rd edn. Cambridge University Press, New York (2007)MATHGoogle Scholar
  19. 19.
    Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964).  https://doi.org/10.1021/ac60214a047 CrossRefGoogle Scholar
  20. 20.
    Saxena, S., Verbeek, J.: Heterogeneous face recognition with CNNs. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 483–491. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-49409-8_40 Google Scholar
  21. 21.
    Schafer, R.W.: What is a Savitzky-Golay filter? [lecture notes]. IEEE Sig. Process. Mag. 28(4), 111–117 (2011)CrossRefGoogle Scholar
  22. 22.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.São Carlos Federal University - UFSCARSão CarlosBrazil
  2. 2.UNESP - São Paulo State UniversityBauruBrazil

Personalised recommendations