CFP last date
22 April 2024
Reseach Article

Facial Expression Recognition using Hybrid Transform

by Anmar. A. Razzak, Ahmed Rafid Hashem
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 15
Year of Publication: 2015
Authors: Anmar. A. Razzak, Ahmed Rafid Hashem
10.5120/21142-4166

Anmar. A. Razzak, Ahmed Rafid Hashem . Facial Expression Recognition using Hybrid Transform. International Journal of Computer Applications. 119, 15 ( June 2015), 12-18. DOI=10.5120/21142-4166

@article{ 10.5120/21142-4166,
author = { Anmar. A. Razzak, Ahmed Rafid Hashem },
title = { Facial Expression Recognition using Hybrid Transform },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 15 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number15/21142-4166/ },
doi = { 10.5120/21142-4166 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:07.152618+05:30
%A Anmar. A. Razzak
%A Ahmed Rafid Hashem
%T Facial Expression Recognition using Hybrid Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 15
%P 12-18
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic analysis of facial expressions is rapidly becoming an area of intense interest in computer vision and artificial intelligence research communities. In this paper an approach is presented for facial expression recognition of the six basic prototype expressions (i. e. , happy, surprise, anger, sadness, fear, and disgust) based on Facial Action Coding System (FACS). The approach utilizes the hybrid transform in which consists of two transforms; the Wavelet transform and the Discrete Cosine Transform (DCT). The approach suggested includes many steps such as preprocessing, feature extraction, clustering and recognition. In feature extraction phase the Wavelet transform and the Discrete Cosine Transform (DCT) were implemented, in the clustering phase the Self Organizing Feature Map produced by Kohonen was implanted. Topological ordering patterns produced by Kohonen Self Organizing Map, in which implemented on feature extracted for each prototype facial expression was used to classify the six basic expressions. The map will compute the topological relationship between the particular expressions featured. While in recognition phase Euclidean distance measure had been used. The method tested using FACS-Coded expressions database of basic emotions: "Cohn-Kanade Database". An average recognition rate of 92. 2% was achieved for six basic expressions.

References
  1. Vinay Bettadapura ," Face Expression Recognition and Analysis: The State of the Art", College of Computing, Georgia Institute of Technology.
  2. P. Ekman and W. V. Friesen. Constants Across Cultures in the Face and Emotion. Journal of Personality and Social Psychology, 17(2):124-129, 1971.
  3. B. Fasel ,Juergen Luettin ,"Automatic Facial Expression analysis: a survey",pattern recognition ,2003(36).
  4. Y. l. Tian, T. Kanade, and J. F. Cohn, "Evaluation of Gabor-Wavelet-Based Facial Action Unit Recognition in Image Sequences of Increasing Complexity," in Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 229-234, 2002.
  5. Kaimin Yu ," TOWARDS REALISTIC FACIAL EXPRESSION RECOGNITION" , School of Information Technologies at The University of Sydney, Thesis, PHD, October 2013.
  6. M. Pantic and L. Rothkrantz, "Expert System for Automatic Analysis of Facial Expression", Image and Vision Computing J. , Vol. 18, No. 11, p. 881-905, 2000.
  7. H. Rowley, S. Baluja, T. Kanade, "Neural Network-Based Face Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, p. 23 – 38,1998.
  8. Z. Zeng, Y. Fu, G. I. Roisman, Z. Wen, Y. Hu and T. S. Huang, "Spontaneous Emotional Facial Expression Detection", Journal of Multimedia, Vol. 1, No. 5, p. 1-8, 2006.
  9. Bartlett, M. S. , G. Littlewort, I. Fasel, J. R. Movellan 'Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction' IEEE Workshop on Face Processing in Video, Washington, 2004.
  10. Kwok-Wai Wan, Kin-Man Lam,Kit-Chong Ng,"An accurate active shape model for facial feature extraction", Pattern Recognition Letters ,vol. 26,pp. 2409–2423,May 2005.
  11. Y. Tian, T. Kanade, and J. Cohn" Recognizing action units for facial expression analysis" IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001.
  12. M. J. Black and Y. Yacoob, "Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion," lnt'l J. Computer Vision, Vol. 25, No. 1, p. 23-48, 1997.
  13. G. C. Littlewort, M. S. Bartlett, J. Chenu, I. Fasel, T. Kanda, H. Ishiguro, J. R. Movellan,"Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification", Advances in Neural Information Processing Systems, Vol 16, p. 1563-1570, 2004.
  14. Yacoob Y. , L. Davis. 'Computing spatio-temporal representation of human faces' In CVPR, pages 70–75, Seattle, WA, June 1994.
  15. Feng, G. C. , P. C. Yuen, D. Q. Dai, "Human face recognition using PCA on wavelet subband", Journal of Electronic Imaging -- April 2000 -- Volume 9, Issue 2, pp. 226-233, 2000.
  16. Jun, S. , Z. Qing, W. Wenyuan, "A improved facial recognition system method", ISBN: 978-3-540-41180-2, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, vol. 1948, pp. 212-221, 2000.
  17. Zhang, Z. , M. Lyons. M. Schuster, S. Akamatsu, 'Comparison between geometry based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron', in Proc. IEEE 3rd Int'l Conf. on Automatic Face and Gesture Recognition, Nara, Japan, April 1998.
  18. Stathopoulou, I. O. , G. A. Tsihrintzis, 'An improved neural network-based face detection and facial expression classification system', Proceedings of IEEE SMC, pp. 666–671, 2004.
  19. Cohen, I. , N. Sebe, A. Garg, M. S. Lew, T. S. Huang 'Facial expression recognition from video sequences' Computer Vision and Image Understanding, Volume 91, pp 160 – 187 ISSN: 1077-3142 2003.
  20. Dao-Qing Dai and Hong Yan, "Wavelets and Face Recognition ",Sun Yat-Sen (Zhongshan) University and City University of Hong KongChina.
  21. Wavelet transform. From Wikipedia: http://en. wikipedia. org/wiki/wavelet transform
  22. S. Mallat: "A wavelet Tour of Signal Processing", Academic Press, San Diego 1998.
  23. W. B. Pennebaker and J. L. Mitchell, "JPEG – Still Image Data Compression Standard," Newyork: International Thomsan Publishing, 1993.
  24. JAWAD NAGI," Pattern Recognition Of Simple Shapes In A Matlab / Simulink Environment: Design And Development Of An Efficient High-Speed Face Recognition System", College Of Engineering University Tenaga Nasional , Thesis, M. Sc, 2007,pp (97).
  25. Keerti Keshav Kanchi," Facial Expression Recognition using Image Processing and Neural Network", International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345 Vol. 4 No. 05 May 2013.
  26. Euclidean distance. From Wikipedia: http://en. wikipedia. org/wiki/Euclidean_distance.
Index Terms

Computer Science
Information Sciences

Keywords

DCT wavelet facial expression recognition SOM Euclidean distance.