Gramatical Facial Expression Recognition with Artificial Intelligence Tools

  • Elena AcevedoEmail author
  • Antonio Acevedo
  • Federico Felipe
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


The face is the reflection of our emotions. We can guess the state of mind of a person by observing the face. In this paper, we applied an Associative Model algorithm to recognized Grammatical Facial Expressions. We used the dataset of the Brazilian sign language (Libras) system. The model we applied was a Morphological Associative Memory. We implemented a memory for each expression. The average of recognition for the same expression was of 98.89%. When we compare one expression with the others, we obtained a 98.59%, which means that our proposal confuses few expressions.


Computational intelligence Associative memories Facial expression Pattern recognition 


  1. 1.
    Benitez-Quiroz, F., Wilbur, R., Martinez, A.: The not face: a grammaticalization of facial expressions of emotion. Cognition 150, 77–84 (2016)CrossRefGoogle Scholar
  2. 2.
    Martinez, A., Du, S.: Model of the perception of facial expressions of emotion by humans: research overview and perspectives. J. Mach. Learn. Res. 13, 1589–1608 (2012)MathSciNetGoogle Scholar
  3. 3.
    Freitas, F., Marques, S., Aparecido de Moraes, C., Venancio, F.: Grammatical facial expressions recognition with machine learning. In: Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, pp. 180–185 (2014)Google Scholar
  4. 4.
    Lien, J.J., Kanade, T., Cohn, J.F., Li, C.C.: Automated facial expression recognition based on FACS action units. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition (1998)Google Scholar
  5. 5.
    Kumaria, J., Rajesha, R., Pooja, K.M.: Facial expression recognition: a survey. Procedia Comput. Sci. 58, 486–491 (2015). Second International Symposium on Computer Vision and the InternetCrossRefGoogle Scholar
  6. 6.
    Tang, M., Chen, F.: Facial expression recognition and its application based on curvelet transform and PSO-SVM. Optik 124, 5401–5406 (2013)CrossRefGoogle Scholar
  7. 7.
    Chakrabartia, D., Dutta, D.: Facial expression recognition using eigenspaces. Procedia Technol. 10, 755–761 (2013). International Conference on Computational Intelligence: Modeling Techniques and Applications 2013CrossRefGoogle Scholar
  8. 8.
    Taufeeq, M.: An Ada-Random forests based grammatical facial expressions recognition approach. In: International Conference on Informatics, Electronics & Vision (ICIEV) (2015)Google Scholar
  9. 9.
    Bhuvan, M.S., Vinay, D., Siddharth, J., Ashwin, T.S., Ram, M., Reddy, G., Sutej, P.K.: Detection and analysis model for grammatical facial expressions in sign language. In: IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia (2016)Google Scholar
  10. 10.
    Ritter, G.X., Sussner, P., Diaz de León, J.L.: Morphological associative memories. IEEE Trans. Neural Netw. 9(2), 281–293 (1998)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Elena Acevedo
    • 1
    Email author
  • Antonio Acevedo
    • 1
  • Federico Felipe
    • 1
  1. 1.Instituto Politécnico NacionalMexico CityMexico

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