Human Emotion Recognition Using Body Expressive Feature

  • R. SanthoshkumarEmail author
  • M. Kalaiselvi Geetha
Conference paper


Recognition of emotions from human plays a vital role in our day-to-day life and is essential for social communication. In many application of human–computer interaction using nonverbal communication like facial expression, body movements, eye movements and gestures are used. Among these methods, body movement method is widely used because it predicts the emotions of human. In this paper, body expressive features (angle, distance, velocity and acceleration) are proposed to recognize the emotion from human body movements. The GEMEP corpus (straight view) videos are used for this experiment. The 12-dimensional features were extracted from the head point, left-hand point and right-hand point of body movements of the human present in the frame. The features are given to the random forest (RF) classifier to predict the human emotions. The performance measure can be calculated using qualitative and quantitative analyses.


Emotion recognition Nonverbal communication Body movements Random forest (RF) classifier 


  1. 1.
    Glowinski, D., Mortillaro, M., Scherer, K., Dael, N., Camurri, G.V.A.: Towards a minimal representation of affective gestures. Affect. Comput. Intell. Interaction. IEEE 498–504 (2015)Google Scholar
  2. 2.
    Castellano, G., Villalba, S.D., Camurri, A.: Recognizing human emotions from body movement and gesture dynamics. Int. Conf. Affect. Comput. Intell. Interact., Springer 71–82 (2007)Google Scholar
  3. 3.
    Santhoshkumar, R., Geetha, M.K., Arunnehru, J.: SVM—KNN based emotion recognition of human in video using HOG feature and KLT tracking algorithm. Int. J. Pure Appl. Math. 117(15), 621–634 (2017)Google Scholar
  4. 4.
    Shafir, T., Tsachor, R.P., Welch, K.B.: Emotion regulation through movement: unique sets of movement characteristics are associated with and enhance basic emotions. Front. Psychol. 6, 1–15 (2016)CrossRefGoogle Scholar
  5. 5.
    Saha, S., Datta, S., Konar, A., Janarthanan, R.: A study on emotion recognition from body gestures using kinect sensor. Commun. Signal Processing. IEEE 056–060 (2014)Google Scholar
  6. 6.
    Arunnehru, J., Kalaiselvi Geetha, M.: Motion intensity code for action recognition in video using PCA and SVM. Min. Intell. Knowl. Explor. 8284, 70–81 (2013)Google Scholar
  7. 7.
    Arunnehru, J., Kalaiselvi Geetha, M.: Behavior recognition in surveillance video using temporal features. In: 4th ICCCNT, Thiruchengode, India (2013)Google Scholar
  8. 8.
    J. Arunnehru., M. Kalaiselvi Geetha., Automatic Activity Recognition for Video Surveillance. International Journal of Computer Application. Vol.75, 9, 1–6 (2013)CrossRefGoogle Scholar
  9. 9.
    J. Arunnehru., M. Kalaiselvi Geetha., Automatic human emotion recognition in surveillance video. Intelligent Techniques in Signal Processing for Multimedia Security, pp. 321–342. Springer (2017)Google Scholar
  10. 10.
    Varghese, A.A., Cherian, J.P., Kizhakkethottam, J.J.: Overview on emotion recognition system. In: International Conference on Soft-Computing and Network Security (2015)Google Scholar
  11. 11.
    Piana, S., Stagliano, A., Odone, F., Verri, A., Camurri, A.: Real-time automatic emotion recognition from body gestures. Human-Computer Interaction. Computer Vision and Pattern Recognition (2014)Google Scholar
  12. 12.
    Karg, M., Samadani, A.A., Gorbet, R., Kühnlenz, K., Hoey, J., Kulić, D.: Body movements for affective expression: a survey of automatic recognition and generation. IEEE Trans. Affect. Comput. 4, 4 (2013)CrossRefGoogle Scholar
  13. 13.
    Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., Scherer, K.: Toward a minimal representation of affective gestures. IEEE Trans. Affect. Comput. 2(2) (2011)CrossRefGoogle Scholar
  14. 14.
    Wang, W., Enescu, V., Sahli, H.: Adaptive real-time emotion recognition from body movements. ACM Trans. Interact. Intell. Syst. 5(4) (2015)CrossRefGoogle Scholar
  15. 15.
    Fourati, N., Pelachaud, C.: Multi-level classification of emotional body expression. IEEE (2015)Google Scholar
  16. 16.
    Prinzie, A., Van den Poel, D., Random Forests for multiclass classification: random multinomial logit. Expert Syst. Appl. 34(3), 1721–1732CrossRefGoogle Scholar
  17. 17.
    Acharjya, D.P., Geetha, M.K. Sanyal, S.: Internet of Things: Novel Advances and Envisioned Applications. Springer International Publishing, USA: Springer. ISBN 978-3-319-53470-1, ISSN 2197-6511, pp. 1–399. (2017)Google Scholar
  18. 18.
    Kalaiselvi Geetha, M., Palanivel, S.: Video classification and shot detection for video retrieval applications. Int. J. Comput. Intell. Syst. 2(1), 39–50 (2009)CrossRefGoogle Scholar
  19. 19.
    Chitra, M., Geetha, M.K., Menaka, L.: Occlusion and abondoned object detection for Surveillance applications. Int. J. Comput. Appl. Technol. Res. 2(6), 708–713 (2013)CrossRefGoogle Scholar
  20. 20.
    Rajesh, P., Geetha, M.K., Ramu, R.: Traffic density estimation, vehicle classification and stopped vehicle detection for traffic surveillance system using predefined traffic videos. Int. J. Elixir Comput. Sci. Eng. 56, Number A, 13671–13676 (2013)Google Scholar
  21. 21.
    Punitha, A., Kalaiselvi Geetha, M., Sivaprakash, A.: Driver fatigue monitoring system based on eye state analysis. In: International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], IEEE, pp. 1405–1408 (2014)Google Scholar
  22. 22.
    Bänziger, T., Mortillaro, M., Scherer, K.R.: Introducing the geneva multimodal expression corpus for experimental research on emotion perception. Emotion 12(5), 1161–1179 (2012)CrossRefGoogle Scholar
  23. 23.
    Bänziger, T., Scherer, K.R.: Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) corpus. In: Blueprint for Affective Computing: A Sourcebook Oxford. England: Oxford University Press. 271–294 (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChennaiIndia

Personalised recommendations