Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision

Amel Aissaoui, Jean Martinet

Abstract

This paper introduces a bi-modal face recognition approach. The objective is to study how combining depth and intensity information can increase face recognition precision. In the proposed approach, local features based on LBP (Local Binary Pattern) and DLBP (Depth Local Binary Pattern) are extracted from intensity and depth images respectively. Our approach combines the results of classifiers trained on extracted intensity and depth cues in order to identify faces. Experiments are performed on three datasets: Texas 3D face dataset, BOSPHORUS 3D face dataset and FRGC 3D face dataset. The obtained results demonstrate the enhanced performance of the proposed method compared to mono-modal (2D or 3D) face recognition. Most processes of the proposed system are performed automatically. It leads to a potential prototype of face recognition using the latest RGB-D sensors, such as Microsoft Kinect or Intel RealSense 3D Camera.

References

  1. Abate, A. F., Nappi, M., Riccio, D., and Sabatino, G. (2007). 2D and 3D face recognition: A survey. Pattern Recognition Letters, 28(14):1885 - 1906.
  2. Aissaoui, A., Martinet, J., and Djeraba, C. (2014). Dlbp: A novel descriptor for depth image based face recognition. In Proceedings of the 21th IEEE international conference on Image processing, pages 298-302.
  3. Bowyer, K. W., Chang, K., and Flynn, P. (2006). A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding, 101(1):1-15.
  4. Byun, H. and Lee, S.-W. (2002). Applications of support vector machines for pattern recognition: A survey. In Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 7802, pages 213-236, London, UK, UK. Springer-Verlag.
  5. Chang, K., Bowyer, K., and Flynn, P. (2003). Face recognition using 2D and 3D facial data. In ACM Workshop on Multimodal User Authentication, pages 25- 32. Citeseer.
  6. Fan, K.-C. and Hung, T.-Y. (2014). A novel local pattern descriptor - local vector pattern in high-order derivative space for face recognition. IEEE Transactions on Image Processing, 23(7):2877-2891.
  7. Gupta, S., Castleman, K., Markey, M., and Bovik, A. (2010). Texas 3D face recognition database. In Image Analysis and Interpretation. IEEE Southwest Symposium on, pages 97-100. IEEE.
  8. Huang, D., Ardabilian, M., Wang, Y., and Chen, L. (2009). Asymmetric 3D/2D face recognition based on lbp facial representation and canonical correlation analysis. In Proceedings of the 16th IEEE international conference on Image processing, ICIP'09, pages 3289- 3292, Piscataway, NJ, USA. IEEE Press.
  9. Huang, D., Shan, C., Ardabilian, M., Wang, Y., and Chen, L. (2011). Local binary patterns and its application to facial image analysis: A survey. IEEE Transactions 100 98 )% 96 (
  10. tae 94 r
  11. ino 92 t
  12. ign 90 o
  13. ceR 88 86 84 on Systems, Man, and Cybernetics, Part C, 41(6):765- 781.
  14. Huang, Y., Wang, Y., and Tan, T. (2006). Combining statistics of geometrical and correlative features for 3D face recognition. In Proceedings of the British Machine Vision Conference, pages 879-888.
  15. Hung, T.-Y. and Fan, K.-C. (2014). Local vector pattern in high-order derivative space for face recognition. In Proceedings of the 21th IEEE international conference on Image processing, pages 239-3243.
  16. Husken, M., Brauckmann, M., Gehlen, S., and Von der Malsburg, C. (2005). Strategies and benefits of fusion of 2D and 3D face recognition. In Computer Vision and Pattern Recognition-Workshops. IEEE Computer Society Conference on, pages 174-174. IEEE.
  17. Jahanbin, S., Choi, H., and Bovik, A. (2011). Passive multimodal 2-d+3-d face recognition using gabor features and landmark distances. Information Forensics and Security, IEEE Transactions on, 6(4):1287-1304.
  18. Mantecn, T., del Blanco, C., Jaureguizar, F., and Garca, N. (2014). Dlbp: A novel descriptor for depth image based face recognition. In Proceedings of the 21th IEEE international conference on Image processing, pages 293-297.
  19. Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence., 24(7):971-987.
  20. Ojala, T., Valkealahti, K., Oja, E., and Pietikinen, M. (2001). Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition, 34(3):727 - 739.
  21. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. (2005). Overview of the face recognition grand challenge. In Computer vision and pattern recognition. IEEE computer society conference on, volume 1, pages 947-954. IEEE.
  22. Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. J. (2000). The feret evaluation methodology for facerecognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10):1090- 1104.
  23. Savran, A., Alyüz, N., Dibekliog?lu, H., C¸ eliktutan, O., Gökberk, B., Sankur, B., and Akarun, L. (2008). Bosphorus database for 3D face analysis. In Biometrics and Identity Management, pages 47-56. Springer.
  24. Xu, C., Li, S., Tan, T., and Quan, L. (2009). Automatic 3D face recognition from depth and intensity gabor features. Pattern Recognition, 42(9):1895 - 1905.
  25. Xu, L., Krzyzak, A., and Suen, C. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. Systems, Man and Cybernetics, IEEE Transactions on, 22(3):418-435.
  26. Zhang, B., Gao, Y., Zhao, S., and Liu, J. (2010). Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. Trans. Img. Proc., 19(2):533-544.
  27. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. (2003). Face recognition: A literature survey. Acm Computing Surveys, 35(4):399-458.
Download


Paper Citation


in Harvard Style

Aissaoui A. and Martinet J. (2015). Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 559-564. DOI: 10.5220/0005359605590564


in Bibtex Style

@conference{visapp15,
author={Amel Aissaoui and Jean Martinet},
title={Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={559-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005359605590564},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Bi-modal Face Recognition - How combining 2D and 3D Clues Can Increase the Precision
SN - 978-989-758-090-1
AU - Aissaoui A.
AU - Martinet J.
PY - 2015
SP - 559
EP - 564
DO - 10.5220/0005359605590564