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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 12    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 12, December 2011
Artificial Neural Network Design and Parameter Optimization for Facial Expressions Recognition
Full Text(PDF, 3000)  PP.  
Author(s)
Ammar A. Alzaydi
KEYWORDS
Artificial Neural Network, Design, Facial Expressions, Optimization, Parameter Optimization, Recognition
ABSTRACT
This paper presents an Artificial Neural Network design and Neural Network parameter optimization for emotional recognition of classified facial expressions. The main goal in this paper is to teach computers to recognize three distinct human emotions from static images. Training and Testing dataset will be collected and a multilayer perceptron network will be built to implement an emotion classifier. Two excellent face databases are used to construct the training and testing datasets. Cross-validation techniques were used to compare the parameters of the Neural Network classifier and the types of activation functions. This paper shows that the performance of the designed Neural Network is very high at above 90% with around 60/40 ratio of training dataset size to test dataset size.
References
[1] Belhumeur, P N., and D J. Kriegman. “The Yale Face Da-tabase”. 1997. Yale University, New Haven. 27 Mar. 2008 .

[2] Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi & Jiro Gyoba. “Coding Facial Expressions with Gabor Wavelets”. Proceedings, Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp. 200-205.

[3] Hancock, Peter. “Psychological Image Collection at Stir-ling (PICS)”. University of Stirling, Stirling. 28 Mar. 2008 .

[4] Lisetti and D. Rumelhart. ""Facial Expression Recognition using a Neural Network"". In Proceedings of the 11 th International Flairs Conference. AAAI Press, 1998.

[5] Jyh-Yeong Chang, and Jia-Lin Chen. “A facial expression recognition system using neural networks”. IJCNN '99. International Joint Conference on Neural Networks, 1999.

[6] Franco, Leonardo, and Alessandro Treves. ""A Neural Network Facial Expression Recognition System using Unsupervised Local Processing. ""Image and Signal Processing and Analysis” (2001): 628-32. 31 Mar. 2008

[7] Gu, L. and Kanade, T. 3D Alignment of Face in a Single Image, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, 2006.

[8] Zhou, Y., Gu, L. and Zhang, H. Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Wisconsin, 2003.

[9] Bishop, Christopher. “Neural Networks for Pattern Rec-ognition”. London: Oxford University Press. 1995.

[10] Hopfield, J. (1984) “Neurons with graded response have collective computational properties like those of two state neurons,” in Proceedings of the National Academy of Science, pp. 3088-92.

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