AI Desktop Partner Facial Expression Detection

Authors

  • AS Gawade Department of Computer Engineering, S.S.P.M College, Kankavli, India
  • YC Gaikwad Department of Computer Engineering, S.S.P.M College, Kankavli, India
  • AT Lambar Department of Computer Engineering, S.S.P.M College, Kankavli, India

DOI:

https://doi.org/10.26438/ijcse/v8i3.7577

Keywords:

Desktop partner, stress relief, emotion detection

Abstract

In this paper focuses on a system of recognizing human’s emotion detected from a human’s face. The analysed information is conveyed by the regions of the eye’s and the mouth and the image is compared with the database created which consist of various facial expressions pertaining to six universal basic facial emotions. The methodology uses a classification technique of information into a new fused image which is composed of two blocks integrated by the area of the eyes and mouth, very sensitive areas to changes human’s expressions. This system focuses on the facial expressions and by detecting them it helps to relieve the stress of the user by providing the various platforms such as the Chat Bot, Music Player, etc. this is based on the detected expressions of the user and the system uses the machine learning for this purpose.

References

[1] A. Mehrabian, “Communication without words”, psychology today, vol. 2, no. 4, pp. 53-56, 1968. H. Simpson, Dumb Robots, 3rd ed., Springfield: UOS Press, 2004, pp.6-9.

[2] V. Bruce, “What the Human Face Tells the Human Mind: Some Challenges for the Robot-Human Interface”, Proc. IEEE Int. Workshop Robot and Human Communication, pp. 44-51, 1992B. Simpson, et al, “Title of paper goes here if known,” unpublished.

[3] Neha Gupta, Prof. Navneet Kaur, “Design and Implementation of Emotion Recognition System by Using Matlab”, International Journal of Engineering Research and Applications, Vol. 3, Issue 4, pp. 2002-2006, Jul-Aug 2013.

[4] P. M. Chavan, M. C. Jadhav, J. B. Mashruwala, A. K. Nehete, Pooja A. Panjari, “Real Time Emotion Recognition through Facial Expressions for Desktop Devices”, International Journal of Emerging Science and Engineering, Vol. 1, No. 7, May 2013 [5] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, Jul. 1997.

[6] B.A. Draper, K. Baek, M.S. Bartlett, J.R. Beveridge, “Recognizing Faces with PCA and ICA,” Computer Vision and Image Understanding: special issue on face recognition, in press.

[7] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional PCA: A new approach to appearance-based face representation and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131–137, 2004.

[8] L.Torres, J. Reutter, and L. Lorente, “The importance of the color information in face recognition,” in Proceedings IEEE International Conference on Image Processing, vol. 3, pp. 627–631, 1999

[9] M. A. O. Vasilescu and D. Terzopoulos, “Multilinear image analysis for facial recognition,” in Proc. Int Conf. Pattern Recognit., Quebec City, QC, Canada, pp. 511–514, Aug. 2002.

[10] A.K. Jain, R.P.W. Duin, J Mao, "Statistical Pattern Recognition: A Review", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 4-37, 2000

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Published

2020-03-30
CITATION
DOI: 10.26438/ijcse/v8i3.7577
Published: 2020-03-30

How to Cite

[1]
A. Gawade, Y. Gaikwad, and A. Lambar, “AI Desktop Partner Facial Expression Detection”, Int. J. Comp. Sci. Eng., vol. 8, no. 3, pp. 75–77, Mar. 2020.

Issue

Section

Research Article