Abstract

Facial expression plays significant role for human beings to communicate their emotions. Automatic facial expression analysis is a flourishing area of research in computer science, and it is also still a challenge. This paper discusses the application of a natural network based facial expression recognition using fisherface. Back propagation neural network is used as a classifier for classifying the expressions. For face portion segmentation and localization, integral projection method is used. The accuracy of system performance have evaluated on a public database "Japanese Female Facial Expression (JAFFE)". The experimental results show the effectiveness of our scheme. The best average recognition rate achieves 89. 20%.

References

• Bashyal, S. and Venayagamoorthy, G. K. 2008. "Recognition of Facial Expressions Using Gabor Wavelets and Learning Vector Quantization", Engineering Application of Artificial Intelligence, Available at http://dx. doi. org/10. 1016/j. engappai. 2007. 11. 010, [Retrieved on 2 January 2010].
• Belhumeur, P. N. Hespanha, J. P. and Kriegman, D. J. 1997. "Eigenfaces vs. Fisherfaces: Recognition Using Class-Specific Linear Projection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720.
• Daw-Tung Lin. 2006 . "Facial Expression Classification Using PCA and Hierarchical Radial Basis Function Network", Journal of Information Science and Engineering, Vol. 22, pp. 1033-1046.
• García-Mateos, G. Ruiz, A. , and Lopez-de-Teruel, P. E. 2002. "Face Detection Using Integral Projection Models", in 9th International Workshop on Structural Syntactic Pattern Recognition, Windsor (Canada).
• Hong-Bo Deng, Lian-Wen Jin, Li-Xin Zhen, and Jian-Cheng Huang. 2005. "A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA", International Journal of Information Technology, Vol. 11, No. 11, pp. 86-96.
• Khandait, S. P. , Thool, R. C. and Khandait, P. D. 2011. "Automatic Facial Feature Extraction and Expression Recognition based on Neural Network", International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 2, No. 1, pp. 113-118.
• Kumar, R. Anjum, B. and Ghiday, Y. 2011. "Role of Communication in Management Teaching & Research in the International Context", Research World - Journal of Arts Science & Commerce, Vol. II, No. 1, pp. 70-77.
• Lajevardi, S. M. and Hussain, Z. M. 2009. "Local Feature Extraction Methods for Facial Expression Recognition", in 17th European Signal Processing Conference (EUSIPCO 2009) Glasgow, Scotland, pp. 60-64.
• Lajevardi, S. M. and Hussain, Z. M. 2009. "Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions", Advances in Electrical and Computer Engineering, Vol. 9, No. 3, pp. 63-67.
• Lekshmi, V. P. and Sasikumar, M. 2008. "A Neural Network Based Facial Expression Analysis using Gabor Wavelets", World Academy and Science, Engineering and Technology, Vol. 42, pp. 563-567.
• Lyons, M. J. Akamatsu, S. Kamachi, M. , and Gyoba, J. 1998. "Coding Facial Expressions with Gabor Wavelets", in the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205.
• Mazanec, J. Melis?k, M. Oravec, M. , and Pavlovi?ová, J. 2008. "Support Vector Machines, PCA and LDA in Face Recognition", Journal of Electrical Engineering, Vol. 59, No. 4, pp. 203-209
• Zhao, Y. , Xiaojun Shen, Georganas, N. D. , and Petriu, E. M. 2009. "Part-based PCA for Facial Feature Extraction and Classification", in IEEE International Workshop on Haptic Audio Visual Environments and Games 2009. Issue date: 7-8 November, pp. 99 – 104.