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Reduced Feature Set for Emotion Recognition Based on Angle and Size Information

  • Patrick DunauEmail author
  • Mike Bonny
  • Marco F. Huber
  • Jürgen Beyerer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 867)

Abstract

The correct interpretation of facial emotions is important for many applications like psychology or human-machine interaction. In this paper, a novel set of features for emotion classification from images is introduced. Based on landmark points extracted from the face, angles between point-connecting lines and size information of mouth and eyes are extracted. Experiments compare the quality and reliability of the feature set to landmark-based features and facial action unit based features.

Keywords

Pattern recognition Emotion recognition Classification Feature extraction Neural networks 

Notes

Acknowledgment

The research and development project on which this report is based is being funded by the Federal Ministry of Transport and Digital Infrastructure within the mFUND research initiative.

References

  1. 1.
    Kazemi, V., Sullivan, J.: One Millisecond face alignment with an ensemble of regression trees. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), Columbus, OH, USA, pp. 1867–1874 (2014)Google Scholar
  2. 2.
    Qu, C., Gao, H., Monari, E., Beyerer, J., Thiran, J.-P.: Towards robust cascaded regression for face alignment in the wild. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 1–9 (2015)Google Scholar
  3. 3.
    Jain, S., Hu, C., Aggarwal, J.K.: Facial expression recognition with temporal modeling of shapes. In: Proceedings of the 2011 IEEE Conference on Computer Vision Workshops (ICCV Workshops), pp. 1642–1649 (2011)Google Scholar
  4. 4.
    Huang, X.: Methods for facial expression recognition with applications in challenging situations. Doctoral Dissertation, Acta Universitatis Ouluensis. C, Technica., Number 509 (2014)Google Scholar
  5. 5.
    Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK+): a complete expression dataset for action unit and emotion-specified expression. In: Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA, pp. 94–101 (2010)Google Scholar
  6. 6.
    Tong, Y., Ji, Q.: Exploiting dynamic dependencies among action units for spontaneous facial action recognition. In: Emotion Recognition, pp. 47–67. Wiley (2015)Google Scholar
  7. 7.
    Gower, J.C.: Generalized procrustes analysis. Psychometrika 40(1), 33–51 (1975)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3/4), 169–200 (1992)CrossRefGoogle Scholar
  9. 9.
    Ekman, P.: Basic Emotions. Handbook of Cognition and Emotion, pp. 45–60. Wiley, New York (1999)Google Scholar
  10. 10.
    King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(4), 1755–1758 (2009)Google Scholar
  11. 11.
    Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG 2000), Grenoble, France, pp. 46–53 (2000)Google Scholar
  12. 12.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Lopes, A.T., de Aguiar, E., De Souza, A.F., Oliveira-Santos, T.: Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recognit. 61, 610–628 (2017)CrossRefGoogle Scholar
  14. 14.
    Liu, M., Li, S., Shan, S., Wang, R., and Chen, X.: Deeply learning deformable facial action parts model for dynamic expression analysis. In: Proceedings of the 12th Asian Conference on Computer Vision (ACCV 2014), Singapore (2014)Google Scholar
  15. 15.
    Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Patrick Dunau
    • 1
    Email author
  • Mike Bonny
    • 1
  • Marco F. Huber
    • 1
    • 3
  • Jürgen Beyerer
    • 2
    • 3
  1. 1.USU Software AGKarlsruheGermany
  2. 2.Fraunhofer Institue of Optronics, System Technologies, and Image Exploitation (IOSB)KarlsruheGermany
  3. 3.Karlsruhe Insitute of Technology (KIT), Institute for Anthropomatics and RoboticsKarlsruheGermany

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