Application of improved AAM and probabilistic neural network to facial expression recognition

Journal of Applied Sciences

Volumn 10, Issue 15, 2010, Pages 1572-1579

  Neggaz, N ^a   Besnassi, M ^a   Benyettou, A ^a  

^a UNIVERSITÉ DES SCIENCES ET DE LA TECHNOLOGIE D ORAN MOHAMED BOUDIAF   (Algeria)

Author keywords

Evolutionary algorithms; Facial expression recognition; Improved active appearance model; JAFFE; Probabilistic neural network

Indexed keywords

EVOLUTIONARY ALGORITHMS; FACE RECOGNITION; FEATURE EXTRACTION; IMAGE RECOGNITION; NEURAL NETWORKS;

ACTIVE APPEARANCE MODELS; AUTOMATIC FACIAL EXPRESSION ANALYSIS; EXPRESSION RECOGNITION; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; JAFFE; JAFFE DATABASE; PROBABILISTIC NEURAL NETWORKS;

HUMAN COMPUTER INTERACTION;

EID: 77954809180     PISSN: 18125654     EISSN: 18125662     Source Type: Journal    
DOI: 10.3923/jas.2010.1572.1579     Document Type: Article

References (29)

1. Bartlett, M., P. Viola, T. Sejnowski, L. Larsen, J. Hager and P. Ekman, 1996. Classifying Facial Action. In: Advances in Neural Information Processing Systems, Touretzky, D., M. Mozer, M. Hasselmo (Eds.). Vol. 8, MIT Press, Cambridge, Massachusetts, pp: 823-829.
2. Cootes, T. and C. Taylor, 2004. Statistical Models of Appearance for Computer Vision. Imaging Science and Biomedical Engineering, USA.
3. Cootes, T.F., D.H. Cooper, C.J. Taylor and J. Graham, 1992. A trainable method of parametric shape description. Image Vision Comput, 10: 289-294.
4. Cootes, T.F., G.J. Edwards and C.J. Taylor, 1998. Active appearance models. Proceedings of the European Conference on Computer Vision, 1998, Active Appearance Models, pp: 484-498.
5. Davoine, F., B. Abboud and M. Dang, 2004. Analyse de visages et d'expressions faciales par modele actif d'apparence. Traitement du Signal, 21: 179-193.
6. Ekman, P., E.S. Richard and V.F. Wallace, 1969. Pan-cultural elements in facial displays of emotion. Science, 164: 86-88.
7. Gao, Y., M.K.H. Leung, S.C. Hm andM.W. Tananda, 2003. Facial expression recognition from line-based caricatures. IEEE Trans. Syst. Man Cybernet-Part A Syst. Human, 33: 407-412.
8. Gill, G.S. and J.S. Sohal, 2005. Battlefield decision making: A neural network approach. J. Theor. Applied Inform. Technol, 4: 697-699.
9. Gu, W.F., Y.V. Venkatesh and C. Xiang, 2010. A novel application of self-organizing network for facial expression recognition from radial encoded contours. Soft Comput, 114: 113-122.
10. Guo, G.D. and C.R. Dyer, 2003. Simultaneous feature selection and classifier training via linear programming: A case study for face expression recognition. IEEE Conf. Comput. Vision Pattern Recogn., 1: 346-352.
11. Hoey, J. and J.J. Little, 2000. Representation and recognition of complex human motion. IEEE Conf. Comput. Vision Pattern Recogn., 1: 752-759.
12. Karaboga, D. and B. Akay, 2009. A comparative study of artificial bee colony algorithm. Applied Mathe. Comput, 214: 108-132.
13. Lampinen, J. and Y. Zelinka, 2000. On stagnation of the differential evolution algorithm. Proceedings of the MENDEL Conference, June 7-9, Brno, Czech Republic, pp: 76-83.
14. Lyons, M., S. Akamatsu, M. Kamachi and J. Gyoba, 1998. Coding facial expressions with Gabor wavelets. Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16, Nara, Japan, pp: 200-205.
15. Lyons, M.J., J. Budynek and S. Akamatsu, 1999. Automatic classification of single facial images. TEEE Trans. Pattern Anal. Mach. Intell, 21: 1357-1362.
16. Neggaz, N. and A. Benyettou, 2009. Hybrid models based on biological approaches for speech recognition. Artificial Intel. Rev., 32: 45-57.
17. Pantic, M. and LJ.M. Rothkrantz, 2004. Facial action recognition for facial expression analysis from static face images. IEEE Trans. Syst. Man Cybernet. Part B, 34: 3-3.
18. Praseeda, L. V. and M. Sasikumar, 2008. A neural network based facial expression analysis using gabor wavelets. World Acad. Sci. Eng. Technol., 42: 563-568.
19. Press, W.H., B.P. Flannery, S.A. Teukolsky and W.T. Vettering, 1988. Numerical Recipes in C. Cambridge University Press, Cambridge, UK.
20. Price, K.V., R.M. Storn and J.A. Lampinen, 2005. Differential Evolution: A Practical Approach to Global Optimization. IstEdn., Spring er-Verlag, New York, ISBN-10: 3540209506, pp: 538.
21. Specht, D.F., 1990. Probabilistic neural networks. Neural Network, 3: 109-118.
22. Storn, R. and K. Price, 1997. Differential evolution A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimiz., 11: 341-359.
23. Viola, P. andM. Jones, 2001. Rapid object detection using a boosted cascade of simple feature. IEEE Conf. Comput. Vision Pattern Recogn., 1: 511-518.
24. Wiskott, L., J.M. Fellous, N. Kruger and M.C. Vonder, 1997. Face recognition by bunch graph matching. TEEE Trans. Pattern Anal. Machine Intell, 19: 775-779.
25. Yacoob, Y. and L. Davis, 1994. Recognizing facial expressions by spatiotemporal analysis. Proc. Int. Conf. Pattern Recog., 1: 747-749.
26. Yingli, T., K. Takeo and F.C. Jeffrey, 2001. Recognizing action units for facial expressions analysis. IEEE Trans. Pattern Analysis Machine Intell., 23: 97-115.
27. Zhang, Y., L. Wu, N. Neggaz, S. Wang and G. Wei, 2009. Remote-sensing image classification based on an improved probabilistic neural network. Neural Networks Sensors, 9: 7516-7539.
28. Zhang, Z., M. Lyons, M. Schuster and S. Akamatsu, 1998. Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. Proceedings of the International Conference Automatic Face and Gesture Recognition, IEEE Computer Society Washington, DC, USA., April 14-16, pp: 454-459.
29. Zhang, Z., 1999. Feature-based facial expression recognition: Sensitivity analysis and experiments with a multi-layer perceptron. J. Pattern Recogn. Artif. Intell, 13: 893-911.