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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(4): 388-393 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.57

A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network

Le Hoang Thai, Nguyen Do Thai Nguyen and Tran Son Hai

Abstract—Facial Expression Classification is an interesting research problem in recent years. There are a lot of methods to solve this problem. In this research, we propose a novel approach using Canny, Principal Component Analysis (PCA) and Artificial Neural Network. Firstly, in preprocessing phase, we use Canny for local region detection of facial images. Then each of local region’s features will be presented based on Principal Component Analysis (PCA). Finally, using Artificial Neural Network (ANN) applies for Facial Expression Classification. We apply our proposal method (Canny_PCA_ANN) for recognition of six basic facial expressions on JAFFE database consisting 213 images posed by 10 Japanese female models. The experimental result shows the feasibility of our proposal method.

Index Terms—Artificial Neural Network (ANN), Canny, Facial Expression Classification, Principal Component Analysis (PCA).

L.H. Thai is with the University of Science, Ho Chi Minh city, 7000, Vietnam (email: lhthai@fit.hcmus.edu.vn).
N.D.T.Nguyen is with the University of Pedagogy, Ho Chi Minh city, 7000, Vietnam (email: nguyenndt@hcmup.edu.vn).
T. S. Hai is with the University of Pedagogy, Ho Chi Minh city, 7000, Vietnam (email: haits@hcmup.edu.vn).

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Cite: Le Hoang Thai, Nguyen Do Thai Nguyen and Tran Son Hai, "A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network," International Journal of Machine Learning and Computing vol. 1, no. 4, pp. 388-393, 2011.

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