Wavelet based approach for facial expression recognition

(1) * Zaenal Abidin Mail (Semarang State University & School of Engineering and Advanced Technology, Massey University, Albany, New Zealand, Indonesia)
(2) Alamsyah Alamsyah Mail (Semarang State University, Indonesia)
*corresponding author


Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database.


Wavelet transforms; Backpropagation neural network; Facial expression; Pattern recognition




Article metrics

Abstract views : 26821 | PDF views : 588




Full Text



T. B. Long, L. H. Thai, and T. Hanh, “Facial expression classification method based on pseudo zernike moment and radial basis function network,” in Proceeding of IEEE 3rd Int. Conf. Machine Learning and Computing (ICMLC), Singapore, 2011, pp. 310–313.

S. Bashyal and G. K. Venayagamoorthy, “Recognition of facial expressions using Gabor wavelets and learning vector quantization,” Eng. Appl. Artif. Intell., vol. 21, no. 7, pp. 1056–1064, Oct. 2008.

S. S. Kulkarni, “Facial image based mood recognition using committee neural networks,” University of Akron, 2006.

H. B. Deng, L. W. Jin, L. W. Zhen, and J. C. Huang, “A new facial expression recognition method based on local Gabor filter bank and PCA plus LDA,” Int. J. Inf. Technol., vol. 11, no. 11, pp. 86–96, 2005.

L. Ma and K. Khorasani, “Facial expression recognition using constructive feedforward neural networks,” IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 34, no. 3, pp. 1588–1595, Jun. 2004.

Z. Abidin and A. Harjoko, “A Neural Network based Facial Expression Recognition using Fisherface,” Int. J. Comput. Appl., vol. 59, no. 3, pp. 30–34, Dec. 2012.

R. H. Abiyev and O. Kaynak, “Fuzzy wavelet neural networks for identification and control of dynamic plants - A novel structure and a comparative study,” IEEE Trans. Ind. Electron., vol. 55, no. 8, pp. 3133–3140, Aug. 2008.

S. A. Billings, H. B. Jamaluddin, and S. Chen, “Properties of neural networks with applications to modelling non-linear dynamical systems,” Int. J. Control, vol. 55, no. 1, pp. 193–224, Jan. 1992.

S. Chen and S. A. Billings, “Neural networks for nonlinear dynamic system modelling and identification,” Int. J. Control, vol. 56, no. 2, pp. 319–346, Aug. 1992.

A. T. C. Goh, “Back-propagation neural networks for modeling complex systems,” Artif. Intell. Eng., vol. 9, no. 3, pp. 143–151, 1995.

J. F. Mas and J. J. Flores, “The application of artificial neural networks to the analysis of remotely sensed data,” Int. J. Remote Sens., vol. 29, no. 3, pp. 617–663, Feb. 2008.

M. Reichstein, P. Ciais, D. Papale, R. Valentini, S. Running, N. Viovy, W. Cramer, A. Granier, J. Ogée, V. Allard, M. Aubinet, C. Bernhofer, N. Buchmann, A. Carrara, T. Grünwald, M. Heimann, B. Heinesch, A. Knohl, W. Kutsch, D. Loustau, G. Manca, G. Matteucci, F. Miglietta, J. m. Ourcival, K. Pilegaard, J. Pumpanen, S. Rambal, S. Schaphoff, G. Seufert, J.-F. Soussana, M.-J. Sanz, T. Vesala, and M. Zhao, “Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis,” Glob. Change Biol., vol. 13, no. 3, pp. 634–651, Mar. 2007.

M. Schlerf and C. Atzberger, “Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data,” Remote Sens. Environ., vol. 100, no. 3, pp. 281–294, Feb. 2006.

K. Huarng and T. H.-K. Yu, “The application of neural networks to forecast fuzzy time series,” Phys. Stat. Mech. Its Appl., vol. 363, no. 2, pp. 481–491, May 2006.

D. Ömer Faruk, “A hybrid neural network and ARIMA model for water quality time series prediction,” Eng. Appl. Artif. Intell., vol. 23, no. 4, pp. 586–594, Jun. 2010.

G. P. Zhang, B. E. Patuwo, and M. Y. Hu, “A simulation study of artificial neural networks for nonlinear time-series forecasting,” Comput. Oper. Res., vol. 28, no. 4, pp. 381–396, Apr. 2001.

L. Liang and D. Wu, “An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network,” Comput. Oper. Res., vol. 32, no. 5, pp. 1115–1129, May 2005.

R. Prabhavalkar and E. Fosler-Lussier, “Backpropagation training for multilayer conditional random field based phone recognition,” in 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, pp. 5534–5537.

J. Sutha and N. Ramaraj, “Neural network based offline tamil handwritten character recognition system,” in Proceeding International Conference on Conference on Computational Intelligence and Multimedia Applications, 2007, 2007, vol. 2, pp. 446–450.

K. Shihab, “A Backpropagation Neural Network for Computer Network Security,” J. Comput. Sci., vol. 2, no. 9, pp. 710–715, Sep. 2006.

M. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, “Coding facial expressions with Gabor wavelets,” in Proceeding the third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200–205.

R. J. Erb, “Introduction to Backpropagation Neural Network Computation,” Pharm. Res., vol. 10, no. 2, pp. 165–170, Feb. 1993.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” Sep. 1985.

D. Svozil, V. Kvasnicka, and J. Pospichal, “Introduction to multi-layer feed-forward neural networks,” Chemom. Intell. Lab. Syst., vol. 39, no. 1, pp. 43–62, Nov. 1997.

Y. Zhang and Q. Ji, “Active and dynamic information fusion for facial expression understanding from image sequences,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 5, pp. 699–714, May 2005.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0