Spontaneous Facial Expression Recognition using Sparse Representation

Dawood Al Chanti, Alice Caplier

2017

Abstract

Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods.

References

  1. Bradley, D. M. and Bagnell, J. A. (2008). Differential sparse coding.
  2. Buciu, I. and Pitas, I. (2004). Application of non-negative and local non negative matrix factorization to facial expression recognition. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 1, pages 288-291. IEEE.
  3. Candes, E. and Romberg, J. (2005). l1-magic: Recovery of sparse signals via convex programming. URL: www. acm. caltech. edu/l1magic/downloads/l1magic. pdf, 4:46.
  4. Candès, E. J., Romberg, J., and Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. Information Theory, IEEE Transactions on, 52(2):489-509.
  5. Donoho, D. L. (2006). Compressed sensing. Information Theory, IEEE Transactions on, 52(4):1289-1306.
  6. Ekman, P. (1999). Basic emotions. Handbook of cognition and emotion, 98:45-60.
  7. El Kaliouby, R. and Robinson, P. (2005). Real-time inference of complex mental states from facial expressions and head gestures. In Real-time vision for humancomputer interaction, pages 181-200. Springer.
  8. Elad, M. and Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. Image Processing, IEEE Transactions on, 15(12):3736-3745.
  9. Hamester, D., Barros, P., and Wermter, S. (2015). Face expression recognition with a 2-channel convolutional neural network. In 2015 International Joint Conference on Neural Networks (IJCNN), pages 1-8. IEEE.
  10. Hoyer, P. O. (2003). Modeling receptive fields with nonnegative sparse coding. Neurocomputing, 52:547- 552.
  11. Jiang, Z., Lin, Z., and Davis, L. S. (2013). Label consistent k-svd: Learning a discriminative dictionary for recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(11):2651-2664.
  12. Kanade, T., Cohn, J. F., and Tian, Y. (2000). Comprehensive database for facial expression analysis. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, pages 46-53. IEEE.
  13. Lyons, M., Akamatsu, S., Kamachi, M., and Gyoba, J. (1998). Coding facial expressions with gabor wavelets. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pages 200-205. IEEE.
  14. Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research, 11:19- 60.
  15. Mairal, J., Elad, M., and Sapiro, G. (2008). Sparse representation for color image restoration. Image Processing, IEEE Transactions on, 17(1):53-69.
  16. Peng, X., Zou, B., Tang, L., and Luo, P. (2009). Research on dynamic facial expressions recognition. Modern Applied Science, 3(5):31.
  17. Rubinstein, R., Bruckstein, A. M., and Elad, M. (2010). Dictionaries for sparse representation modeling. Proceedings of the IEEE, 98(6):1045-1057.
  18. Rubinstein, R., Zibulevsky, M., and Elad, M. (2008). Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit. CS Technion, 40(8):1-15.
  19. Sanghai, K., Su, T., Dy, J., and Kaeli, D. (2005). A multinomial clustering model for fast simulation of computer architecture designs. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 808-813. ACM.
  20. Scherer, K. R. and Ekman, P. (1982). Handbook of methods in nonverbal behavior research, volume 2. Cambridge University Press Cambridge.
  21. Sulic, V., Pers?, J., Kristan, M., and Kovacic, S. (2010). Efficient dimensionality reduction using random projection. In 15th Computer Vision Winter Workshop, pages 29-36.
  22. Tcherkassof, A., Dupré, D., Meillon, B., Mandran, N., Dubois, M., and Adam, J.-M. (2013). Dynemo: A video database of natural facial expressions of emotions. The International Journal of Multimedia & Its Applications, 5(5):61-80.
  23. Tong, Y., Liao, W., and Ji, Q. (2007). Facial action unit recognition by exploiting their dynamic and semantic relationships. IEEE Transactions on Pattern Analysis & Machine Intelligence, (10):1683-1699.
  24. Tropp, J. A. (2004). Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information theory, 50(10):2231-2242.
  25. Tsagkatakis, G. and Savakis, A. (2009). A random projections model for object tracking under variable pose and multi-camera views. In Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on, pages 1-7. IEEE.
  26. Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.
  27. Vempala, S. S. (2005). The random projection method, volume 65. American Mathematical Soc.
  28. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). Locality-constrained linear coding for image classification. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 3360-3367. IEEE.
  29. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y. (2009). Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2):210-227.
  30. Yang, J., Yu, K., Gong, Y., and Huang, T. (2009). Linear spatial pyramid matching using sparse coding for image classification. InComputer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1794-1801. IEEE.
  31. Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2879-2886. IEEE.
Download


Paper Citation


in Harvard Style

Al Chanti D. and Caplier A. (2017). Spontaneous Facial Expression Recognition using Sparse Representation . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 64-74. DOI: 10.5220/0006118000640074


in Bibtex Style

@conference{visapp17,
author={Dawood Al Chanti and Alice Caplier},
title={Spontaneous Facial Expression Recognition using Sparse Representation},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={64-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006118000640074},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Spontaneous Facial Expression Recognition using Sparse Representation
SN - 978-989-758-226-4
AU - Al Chanti D.
AU - Caplier A.
PY - 2017
SP - 64
EP - 74
DO - 10.5220/0006118000640074