Advertisement

3D Research

, 10:14 | Cite as

Automatic Facial Expression Recognition Using Combined Geometric Features

  • Garima SharmaEmail author
  • Latika Singh
  • Sumanlata Gautam
3DR Express
  • 46 Downloads

Abstract

This study presents a geometric feature based automatic facial expression recognition system. The proposed system utilises the facial landmark points to determine the relative distances between the facial features in order to capture deformities caused by the movement of facial muscles due to different expressions. Three feature sets are generated by using landmark coordinates, relative distances between the facial points and a combination of both. Discriminating power of each feature set is determined by training different classification models for classifying an image into six basic emotions or neutral state. The proposed system is validated on two publically available facial expression databases. Experimental results show good accuracy of 95.5% for MUG database on the combined features by using ensemble neural network.

Keywords

Facial expression recognition Feature extraction Facial landmarks Multi-class classification 

Notes

References

  1. 1.
    Ahonen, T., Rahtu, E., Ojansivu, V., & Heikkila, J. (2009). Recognition of blurred faces using Local Phase Quantization.  https://doi.org/10.1109/icpr.2008.4761847.CrossRefGoogle Scholar
  2. 2.
    Aifanti, N., Papachristou, C., & Delopoulos, A. (2010). The MUG facial expression database. In Workshop image analysis for multimedia interactive services (WIAMIS).Google Scholar
  3. 3.
    Ali, G., Iqbal, M. A., & Choi, T. S. (2016). Boosted NNE collections for multicultural facial expression recognition. Pattern Recognition.  https://doi.org/10.1016/j.patcog.2016.01.032.CrossRefGoogle Scholar
  4. 4.
    Arfan, M. (2017). Facial expression recognition using hybrid texture features based ensemble classifier. International Journal of Advanced Computer Science and Applications.  https://doi.org/10.14569/ijacsa.2017.080660.CrossRefGoogle Scholar
  5. 5.
    Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2014). Incremental face alignment in the wild. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.  https://doi.org/10.1109/CVPR.2014.240.
  6. 6.
    Boughrara, H., Chtourou, M., Ben Amar, C., & Chen, L. (2016). Facial expression recognition based on a MLP neural network using constructive training algorithm. Multimedia Tools and Applications.  https://doi.org/10.1007/s11042-014-2322-6.CrossRefGoogle Scholar
  7. 7.
    Cament, L. A., Galdames, F. J., Bowyer, K. W., & Perez, C. A. (2015). Face recognition under pose variation with local Gabor features enhanced by active shape and statistical models. Pattern Recognition.  https://doi.org/10.1016/j.patcog.2015.05.017.CrossRefGoogle Scholar
  8. 8.
    Carcagnì, P., Del Coco, M., Leo, M., & Distante, C. (2015). Facial expression recognition and histograms of oriented gradients: A comprehensive study. SpringerPlus.  https://doi.org/10.1186/s40064-015-1427-3.CrossRefGoogle Scholar
  9. 9.
    Danielsson, P. E. (1980). Euclidean distance mapping. Computer Graphics and Image Processing.  https://doi.org/10.1016/0146-664X(80)90054-4.CrossRefGoogle Scholar
  10. 10.
    Drouard, V., Ba, S., & Horaud, R. (2017). Switching linear inverse-regression model for tracking head pose. In Proceedings2017 IEEE winter conference on applications of computer vision, WACV 2017.  https://doi.org/10.1109/WACV.2017.142.
  11. 11.
    Ekman, P. (1994). Strong evidence for universals in facial expressions: A reply to Russell’s mistaken critique. Psychological Bulletin.  https://doi.org/10.1037/0033-2909.115.2.268.CrossRefGoogle Scholar
  12. 12.
    Ghimire, D., Lee, J., Li, Z. N., & Jeong, S. (2017). Recognition of facial expressions based on salient geometric features and support vector machines. Multimedia Tools and Applications.  https://doi.org/10.1007/s11042-016-3428-9.CrossRefGoogle Scholar
  13. 13.
    Huang, X., Wang, S. J., Zhao, G., & Piteikainen, M. (2015). Facial micro-expression recognition using spatiotemporal local binary pattern with integral projection. In Proceedings of the IEEE international conference on computer vision.  https://doi.org/10.1109/ICCVW.2015.10.
  14. 14.
    Kumar, P., Happy, S. L., & Routray, A. (2017). A real-time robust facial expression recognition system using HOG features. In International conference on computing, analytics and security trends, CAST 2016.  https://doi.org/10.1109/CAST.2016.7914982.
  15. 15.
    Kumari, J., Rajesh, R., & Pooja, K. M. (2015). Facial expression recognition: A survey. In Procedia computer science.  https://doi.org/10.1016/j.procs.2015.08.011.
  16. 16.
    Liu, Q., Yang, J., Deng, J., & Zhang, K. (2017). Robust facial landmark tracking via cascade regression. Pattern Recognition.  https://doi.org/10.1016/j.patcog.2016.12.024.CrossRefGoogle Scholar
  17. 17.
    Lyons, M. J., Akamatsu, S., Kamachi, M., Gyoba, J., & Budynek, J. (1998). The Japanese female facial expression (JAFFE) database. In Proceedings of third international conference on automatic face and gesture recognition.Google Scholar
  18. 18.
    Martinez, B., Valstar, M. F., Jiang, B., & Pantic, M. (2017). Automatic analysis of facial actions: A survey. IEEE Transactions on Affective Computing.  https://doi.org/10.1109/TAFFC.2017.2731763.CrossRefGoogle Scholar
  19. 19.
    Mollahosseini, A., Chan, D., & Mahoor, M. H. (2016). Going deeper in facial expression recognition using deep neural networks. In 2016 IEEE winter conference on applications of computer vision, WACV 2016.  https://doi.org/10.1109/WACV.2016.7477450.
  20. 20.
    Pourebadi, M., & Ourebadi, M. (2016). MLP neural network based approach for facial expression analysis. In International conference on image processing, computer vision, and pattern recognition, IPCV’16.Google Scholar
  21. 21.
    Qayyum, H., Majid, M., Anwar, S. M., & Khan, B. (2017). Facial expression recognition using stationary wavelet transform features. Mathematical Problems in Engineering.  https://doi.org/10.1155/2017/9854050.CrossRefGoogle Scholar
  22. 22.
    Sabharwal, H., & Tayal, A. (2014). Human face recognition. International Journal of Computer Applications.  https://doi.org/10.5120/18243-9173.CrossRefGoogle Scholar
  23. 23.
    Tan, P.-N., Steinbach, M., & Kumar, V. (2013). Introduction to data mining (new international editon). Inform.  https://doi.org/10.1017/CBO9781107415324.004.CrossRefGoogle Scholar
  24. 24.
    Verbaeten, S., & Van Assche, A. (2003). Ensemble methods for noise elimination in classification problems. In International workshop on multiple classifier systems (pp. 317–325). Berlin: Springer.Google Scholar
  25. 25.
    Wang, R. R., Huang, T., & Zhong, J. (2002). Generative and discriminative face modelling for detection. In Proceedings5th IEEE international conference on automatic face gesture recognition, FGR 2002.  https://doi.org/10.1109/AFGR.2002.1004167.
  26. 26.
    Xiong, X., & De La Torre, F. (2013). Supervised descent method and its applications to face alignment. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition.  https://doi.org/10.1109/CVPR.2013.75.
  27. 27.
    Yang, X., Huang, D., Wang, Y., & Chen, L. (2015). Automatic 3D facial expression recognition using geometric scattering representation. In 2015 11th IEEE international conference and workshops on automatic face and gesture recognition, FG 2015.  https://doi.org/10.1109/FG.2015.7163090.
  28. 28.
    Chen, J., Chen, D., Gong, Y., Yu, M., Zhang, K., & Wang, L. (2012). Facial expression recognition using geometric and appearance features. In Proceedings of the 4th international conference on internet multimedia computing and service (pp. 29–33). ACM.Google Scholar

Copyright information

© 3D Display Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The NorthCap UniversityGurgaonIndia
  2. 2.Ansal UniversityGurgaonIndia

Personalised recommendations