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A Comparative Study of Machine Learning Techniques for Emotion Recognition

  • Rhea Sharma
  • Harshit Rajvaidya
  • Preksha PareekEmail author
  • Ankit Thakkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)

Abstract

Humans share emotions which they exhibit through facial expressions. Automatic human emotion recognition algorithm in images and videos aims at detection, extraction, and evaluation of these facial expressions. This paper provides a comparison between various multi-class prediction algorithms employed on the Cohn-Kanade dataset (Lucey in The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression, pp. 94–101, 2010 [1]). The different machine learning algorithms can be used to provide emotion recognition task. We have compared the performance of K-nearest neighbors, Support Vector Machine, and neural network.

Keywords

Emotion recognition Machine learning Cross-validation Performance analysis 

References

  1. 1.
    Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 94–101). New York: IEEE.Google Scholar
  2. 2.
    Hemalatha, G., & Sumathi, C. (2014). A study of techniques for facial detection and expression classification. International Journal of Computer Science and Engineering Survey, 5(2), 27.CrossRefGoogle Scholar
  3. 3.
    Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58.CrossRefGoogle Scholar
  4. 4.
    Yow, K. C., & Cipolla, R. (1997). Feature-based human face detection. Image and Vision Computing, 15(9), 713–736.CrossRefGoogle Scholar
  5. 5.
    Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2002). Face recognition by independent component analysis. IEEE Transactions on Neural Networks/a Publication of the IEEE Neural Networks Council, 13(6), 1450.CrossRefGoogle Scholar
  6. 6.
    Turk, M. A., & Pentland, A. P. (1991). Face recognition using eigenfaces. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91 (pp. 586–591). New York: IEEE.Google Scholar
  7. 7.
    Tian, Y.-I., Kanade, T., & Cohn, J. F. (2001). Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 97–115.CrossRefGoogle Scholar
  8. 8.
    Donalek, C. (2011). Supervised and unsupervised learning. In Astronomy Colloquia USA.Google Scholar
  9. 9.
    Cunningham, P., & Delany, S. J. (2007). k-nearest neighbour classifiers. Multiple Classifier Systems, 34, 1–17.Google Scholar
  10. 10.
    Ding, C. H., & Dubchak, I. (2001). Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17(4), 349–358.CrossRefGoogle Scholar
  11. 11.
    Durgesh, K. S., & Lekha, B. (2010). Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 12(1), 1–7.Google Scholar
  12. 12.
    Guo, B., Gunn, S. R., Damper, R. I., & Nelson, J. D. (2008). Customizing kernel functions for SVM-based hyperspectral image classification. IEEE Transactions on Image Processing, 17(4), 622–629.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Anand, R., Mehrotra, K., Mohan, C. K., & Ranka, S. (1995). Efficient classification for multiclass problems using modular neural networks. IEEE Transactions on Neural Networks, 6(1), 117–124.CrossRefGoogle Scholar
  14. 14.
    Ekman, P., & Rosenberg, E. L. (1997). What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford: Oxford University Press.Google Scholar
  15. 15.
    Rifkin, R., & Klautau, A. (2004). In defense of one-vs-all classification. Journal of machine learning research, 5(Jan), 101–141.MathSciNetzbMATHGoogle Scholar
  16. 16.
    Bird, S., & Loper, E. (2004). NLTK: The natural language toolkit. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (p. 31). Association for Computational Linguistics.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rhea Sharma
    • 1
  • Harshit Rajvaidya
    • 1
  • Preksha Pareek
    • 1
    Email author
  • Ankit Thakkar
    • 1
  1. 1.Institute of TechnologyNirma UniversityAhmedabadIndia

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