Facial Expression Recognition Using Uniform Local Binary Pattern with Improved Firefly Feature Selection

Keywords: Facial Expression Recognition, Firefly Algorithm, Feature Selection, Optimization


Facial expressions are essential communication tools in our daily life. In this paper, the uniform local binary pattern is employed to extract features from the face. However, this feature representation is very high in dimensionality. The high dimensionality would not only affect the recognition accuracy but also can impose computational constraints. Hence, to reduce the dimensionality of the feature vector, the firefly algorithm is used to select the optimal subset that leads to better classification accuracy. However, the standard firefly algorithm suffers from the risk of being trapped in local optima after a certain number of generations. Hence, this limitation has been addressed by proposing an improved version of the firefly where the great deluge algorithm (GDA) has been integrated. The great deluge is a local search algorithm that helps to enhance the exploitation ability of the firefly algorithm, thus preventing being trapped in local optima. The improved firefly algorithm has been employed in a facial expression system. Experimental results using the Japanese female facial expression database show that the proposed approach yielded good classification accuracy compared to state-of-the-art methods. The best classification accuracy obtained by the proposed method is 96.7% with 1230 selected features, whereas, Gabor-SRC method achieved 97.6% with 2560 features.


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Author Biographies

Abdulla M.K. Elmadhoun, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
Abdulla Elmadhoun received his B.Sc. degree from the Engineering Department in the Islamic University of Gaza in 2009. He worked as a software developer for about three years. Currently he is a master student in National University of Malaysia (UKM), Bangi, Selangor, Malaysia. His current research interests include Pattern Recognition, Facial expression recognition, Bio-inspired algorithms, and optimization. 
Md Jan Nordin, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
Md. Jan Nordin  received his B.S. and M.S. degrees in Computer Science from Ohio University, Athens, OH, USA, in 1982 and 1985, respectively, and his Ph.D. degree in Engineering Information Technology from Sheffield Hallam University, South Yorkshire, U.K., in 1995. He is currently an Associate Professor with the Centre for Artificial Intelligence Technology, National University of Malaysia (UKM), Bangi, Selangor, Malaysia. His current research interests include Pattern Recognition, Computer Vision, Intelligent Systems, and Image Reconstruction.


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How to Cite
Elmadhoun, A. M. and Nordin, M. J. (2018) “Facial Expression Recognition Using Uniform Local Binary Pattern with Improved Firefly Feature Selection”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 6(1), pp. 23-32. doi: 10.14500/aro.10378.