2D-3D Face Recognition via Restricted Boltzmann Machines

Xiaolong Wang, Vincent Ly, Rui Guo, Chandra Kambhamettu

Abstract

This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate a shared space based on correlation learning. Then several different correlation learning schemes are evaluated against the proposed scheme. We evaluate the advocated approach on a popular face dataset-FRGCV2.0. Experimental results demonstrate that the latent features extracted using RBMs are effective in improving the performance of correlation mapping for 2D-3D face recognition.

References

  1. Bengio, Y. (2009). Learning deep architectures for ai. Foundations and trends R in Machine Learning, 2(1):1- 127.
  2. Dhillon, P., Foster, D. P., and Ungar, L. H. (2011). Multiview learning of word embeddings via cca. In Advances in Neural Information Processing Systems, pages 199-207.
  3. Guo, G. and Wang, X. (2012). A study on human age estimation under facial expression changes. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2547-2553. IEEE.
  4. Hardoon, D. R., Szedmak, S., and Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16(12):2639-2664.
  5. Hinton, G. E. and Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507.
  6. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3/4):321-377.
  7. Huang, D., Ardabilian, M., Wang, Y., and Chen, L. (2010). Automatic asymmetric 3d-2d face recognition. In Pattern Recognition (ICPR), 2010 20th International Conference on, pages 1225-1228. IEEE.
  8. Huang, D., Ardabilian, M., Wang, Y., and Chen, L. (2012). Oriented gradient maps based automatic asymmetric 3d-2d face recognition. In Biometrics (ICB), 2012 5th IAPR International Conference on, pages 125-131. IEEE.
  9. Jain, A. K., Ross, A. A. A., and Nandakumar, K. (2011). Introduction to biometrics. Springer.
  10. Kim, T.-K., Wong, S.-F., and Cipolla, R. (2007). Tensor canonical correlation analysis for action classification. In Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, pages 1-8. IEEE.
  11. Kumar, N., Berg, A. C., Belhumeur, P. N., and Nayar, S. K. (2009). Attribute and simile classifiers for face verification. In Computer Vision, 2009 IEEE 12th International Conference on, pages 365-372. IEEE.
  12. Li, A., Shan, S., Chen, X., and Gao, W. (2009). Maximizing intra-individual correlations for face recognition across pose differences. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 605-611. IEEE.
  13. Mohamed, A.-r., Dahl, G. E., and Hinton, G. (2012). Acoustic modeling using deep belief networks. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1):14-22.
  14. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 689-696.
  15. 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, 2005. CVPR 2005. IEEE computer society conference on, volume 1, pages 947-954. IEEE.
  16. Rama, A., Tarres, F., Onofrio, D., and Tubaro, S. (2006). Mixed 2d-3d information for pose estimation and face recognition. In Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on, volume 2, pages II-II. IEEE.
  17. Riccio, D. and Dugelay, J.-L. (2005). Asymmetric 3d/2d processing: a novel approach for face recognition. In Image Analysis and Processing-ICIAP 2005, pages 986-993. Springer.
  18. Sargin, M. E., Yemez, Y., Erzin, E., and Tekalp, A. M. (2007). Audiovisual synchronization and fusion using canonical correlation analysis. Multimedia, IEEE Transactions on, 9(7):1396-1403.
  19. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011). Real-time human pose recognition in parts from single depth images. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 1297-1304. IEEE.
  20. Slaney, M. and Covell, M. (2000). Facesync: A linear operator for measuring synchronization of video facial images and audio tracks. In NIPS, pages 814-820.
  21. Srivastava, N. and Salakhutdinov, R. (2012). Multimodal learning with deep boltzmann machines. In Advances in Neural Information Processing Systems 25, pages 2231-2239.
  22. Sutton, R. S. and Barto, A. G. (1998). Reinforcement learning: An introduction, volume 1. Cambridge Univ Press.
  23. Vinokourov, A., Cristianini, N., and Shawe-taylor, J. S. (2002). Inferring a semantic representation of text via cross-language correlation analysis. In Advances in neural information processing systems, pages 1473- 1480.
  24. Wang, H., Klaser, A., Schmid, C., and Liu, C.-L. (2011). Action recognition by dense trajectories. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 3169-3176. IEEE.
  25. Wang, X., Ly, V., Guo, G., and Kambhamettu, C. (2013). A new approach for 2d-3d heterogeneous face recognition. In Multimedia (ISM), 2013 IEEE International Symposium on. IEEE.
  26. 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.
  27. Yang, W., Yi, D., Lei, Z., Sang, J., and Li, S. Z. (2008). 2d-3d face matching using cca. In Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on, pages 1-6. IEEE.
Download


Paper Citation


in Harvard Style

Wang X., Ly V., Guo R. and Kambhamettu C. (2014). 2D-3D Face Recognition via Restricted Boltzmann Machines . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 574-580. DOI: 10.5220/0004736505740580


in Bibtex Style

@conference{visapp14,
author={Xiaolong Wang and Vincent Ly and Rui Guo and Chandra Kambhamettu},
title={2D-3D Face Recognition via Restricted Boltzmann Machines},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={574-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004736505740580},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - 2D-3D Face Recognition via Restricted Boltzmann Machines
SN - 978-989-758-004-8
AU - Wang X.
AU - Ly V.
AU - Guo R.
AU - Kambhamettu C.
PY - 2014
SP - 574
EP - 580
DO - 10.5220/0004736505740580