Human Emotions Recognition Based On Wavelet Transform

  • Diksha Anand Sawant


In this paper, the authors proposes facial expression recognition algorithm of human being based on wavelet transform. The input image is decomposed using discrete wavelet transform (DWT) into different frequency sub-bands. Normally facial emotions are recognized by changes in facial features like changes in eyebrows, eye and mouth etc. But as per human age changes, it is difficult to recognize emotion. So we adopt new approach based on wavelet transform which is little bit different than the other techniques. The objective is to recognize facial expression using energy components of image and efficiently use wavelet transform for calculating energy components of image. The proposed algorithm recognized different human emotions efficiently and energy of spectral component is utilized well.

Keywords: energy component, wavelet transform, image blocks.


1. P. Ekman and W. V. Friesen, Facial Action Coding System, Consulting Psychologists Press, Stanford University, Palo Alto, Calif, USA, 1977.
2. J. C .Hager and, P. Ekman, and W.V. Friesen, Facial Action Coding System, Human Face, Salt Lake City, Utah, USA, 2002.
3. P. Ekman, The Argument and Evidence About Universals in Facial Expression of Emotion, John Wiley & Sons, Hoboken, NJ, USA, 1989.
4. M. Suwa, N. Sugie, and K. Fujimori, “A preliminary note on pattern recognition of human emotional expression,” in Processing of the 4th International 1978.
5. T. Wu, M. S. Bartlett, and J. R. Movellan, “Facial expression recognition using Gabor motion energy filters,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW’10), pp. 42-47, SanFrancisco, Calif, USA, June 2010.
6. Huma Qayyum, Muhammad Majid, Syed Muhammad Anwar, and Bilal Khan3 “Facial Expression Recognition Using Stationary Wavelet Transform Features” Hindawi Mathematical Problems in Engineering Volume 2017, Article ID 9854050, 9 pages
7. G. U. Kharat and S. V. Dudul, “Human emotion recognition system using optimally designed SVM with different facial feature extraction techniques,” WSEAS Transactions on Computers, vol. 7, no. 6, pp. 650-659, 2008.
8. S. B. Kazmi, Qurat-ul-Ain, and M.A. Jaffar, “Wavelets-based facial expression recognition using a bank of support vector machines,” Soft Computing, vol. 16, no. 3, pp. 369-379, 2012.
9. L. Zhang and D. Tjondronegoro, “Facial expression recognition using facial movement features,” IEEE Transaction on Affective Computing, vol. 2, no. 4, pp. 219-229, 2011.
10. Uc, ar, Y. Demir, and C. G. uzelis, “A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering,” Neural Computing and Applications, vol. 27, no. 1, pp. 131-142, 2016.