Advertisement

Abstract

The mental state of a person is judged by detecting smiles. The smile detection starts with face recognition. My algorithm in our paper detects by definition the face from the image we entered, and then it detects the mouth and finally the smile. Next, we make sure that the face we detected is a smiley face or not in a photo, detects the person’s mouth, and decides if they are pleased or not. Given a set of photos of a person entry in our system, we can compare their images using algorithms to detect face and other to detect automatic the corners and the features of the month then we determine which picture has the best smile.

Keywords

Face detection Smile detection Corner detection Haar classifier 

References

  1. 1.
    Murthy, V., Vinay Sankar, T., Padmavarneya, C., Pavankumar, B., Sindhu, K.: Smile detection for user interfaces. Int. J. Resaerch Electron. Commun. Technol., 21–26 (2014)Google Scholar
  2. 2.
    Rai, P., Dixit, M.: Smile detection via Bezier curve of mouth interest points. J. Adv. Res. Comput. Sci. Softw. Eng. 3(7), pp. 1–5 (2013)Google Scholar
  3. 3.
    Devito, J., Meurer, A., Volz, D.: Smile identification via feature recognition and corner detection (2012)Google Scholar
  4. 4.
    Li, J., Chen, J., Chi, Z.: Smile detection in the wild with hierarchical visual feature. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 639–643 (2016)Google Scholar
  5. 5.
    Hu, P., Ramanan, D.: Finding tiny faces. In: Computer Vision and Pattern Recognition, pp. 1612–1624 (2017)Google Scholar
  6. 6.
    Akoum, A.: Real-time best smile detection. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 7(5), pp. 8–12. ISSN 2278-6856 (2018)Google Scholar
  7. 7.
    Davies, E.R.: Face detection and recognition. Chapter in book: Computer Vision, pp. 631–662 (2018)CrossRefGoogle Scholar
  8. 8.
    Bensalem, M.K., Ettabaa, S., Bouhlel, M.S.: Anomaly detection in hyperspectral images based spatial spectral classification. In: International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2016) Hammamet, pp. 166–170, Tunisia (2016)Google Scholar
  9. 9.
    Soliman, H., Saleh, A., Fathi, E.: Face recognition in mobile devices. Int. J. Comput. Appl. (0975 – 8887) 73(2) (2013)CrossRefGoogle Scholar
  10. 10.
    Smari, S.K., Bouhle, M.S.: Gesture recognition system and finger tracking with Kinect. In: International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2016) Hammamet, pp. 44–548, Tunisia (2016)Google Scholar
  11. 11.
    Akoum, A.: Real time hand gesture recognition. Int. J. Eng. Inven. 5(7), 21–30. ISSN 2319-6491 (2016)Google Scholar
  12. 12.
    Ameur, S., Ben Khalifa, A., Bouhle, M.S.: A comprehensive leap motion database for hand gesture recognition. In: International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT 2016) Hammamet, pp. 514–519, Tunisia (2016)Google Scholar
  13. 13.
    Akoum, A.: Real time face detection and segmentation. Int. J. Appl. Eng. Res. 13(19), pp. 14308–14312. ISSN 0973-4562 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alhussain Akoum
    • 1
  • Rabih Makkouk
    • 1
    Email author
  • Rafic Hage Chehade
    • 1
  1. 1.Faculty of Technology, Department of CCNELebanese UniversityBeirutLebanon

Personalised recommendations