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

Abdulla M.K. Elmadhoun; Md Jan Nordin

doi:10.14500/aro.10378

Abdulla M.K. Elmadhoun Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia http://orcid.org/0000-0002-2508-7558
Md Jan Nordin Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia http://orcid.org/0000-0002-7524-5665

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

Abstract

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.

Downloads

Download data is not yet available.

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.

References

Alsalibi, B., Venkat, I., Subramanian, K., Lutfi, S.L. and Wilde, P.D., 2015. The impact of bio-inspired approaches toward the advancement of face recognition. ACM Computing Surveys (CSUR), 48(1), p. 5.

Alsalibi, B., Venkat, I. and Al-Betar, M.A., 2017. A membrane-inspired bat algorithm to recognize faces in unconstrained scenarios. Engineering Applications of Artificial Intelligence, 64, pp.242-260.

Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J., 1996. Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection. In: European Conference on Computer Vision. Springer, Berlin, Heidelberg, pp.43-58.

Bereta, M., Pedrycz, W. and Reformat, M., 2013. Local descriptors and similarity measures for frontal face recognition: Acomparative analysis. Journal of Visual Communication and Image Representation, 24(8), pp.1213-1231.

Dueck, G., 1993. New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104(1), pp.86-92.

Hamid, A.A.K.A. and Nordin, M.J., 2016. Radius based block LBP for facial expression recognition. international information institute (Tokyo). Information, 19(9B), p. 4197.

Happy, S.L. and Routray, A., 2015. Robust Facial Expression Classification using Shape and Appearance Features. In Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on. IEEE, pp.1-5.

He, L. and Huang, S., 2017. Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing, 240, pp.152-174.

International Journal of Computer Applications, 25, pp. 975-8887. Lanitis, A., Taylor, C.J. and Cootes, T.F., 1995. Automatic face identification system using flexible appearance models. Image and Vision Computing, 13(5), pp.393-401.

Jamshidnezhad, A. and Nordin, M.J., 2013. Bee royalty offspring algorithm for improvement of facial expressions classification model. International Journal of Bio-Inspired Computation, 5(3), pp.175-191.

Khatri, N.N., Shah, Z.H. and Patel, S.A., 2014. Facial expression recognition: A survey. International Journal of Computer Science and Information Technologies (IJCSIT), 5(1), pp.149-152.

Lu, X., Kong, L., Liu, M. and Zhang, X., 2015. Facial expression recognition based on gabor feature and SRC. In: Chinese Conference on Biometric Recognition. Springer, Cham, pp.416-422.

Mistry, K., Zhang, L., Sexton, G., Zeng, Y. and He, M., 2017. Facial expression recognition using firefly-based feature optimization, 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, pp.1652-1658.

Ojala, T., Pietikainen, M. and Maenpaa, T., 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), pp.971-987.

Palit, S., Sinha, S.N., Molla, M.A., Khanra, A. and Kule, M. 2011. A Cryptanalytic Attack on the Knapsack Cryptosystem using Binary Firefly Algorithm. Vol. 2. International Conference on Computer and Communication Technology (ICCCT), pp.428-432.

Rathipriya, R., Thangavel, K. and Bagyamani, J., 2011. Binary particle swarm optimization based biclustering of web usage data. arXiv preprint arXiv:1108.0748.

Tilahun, S.L. and Ngnotchouye, J.M.T., 2017. Firefly algorithm for discrete optimization problems: A survey. KSCE Journal of Civil Engineering, 21(2), pp.535-545.

Tilahun, S.L., Ngnotchouye, J.M.T. and Hamadneh, N.N., 2017. Continuous versions of firefly algorithm: A review. Artificial Intelligence Review, 2017, pp.1-48.

Tilahun, S.L. and Ong, H.C., 2012. Modified firefly algorithm. Journal of Applied Mathematics, 2012, pp.12.

Turk, M. and Pentland, A., 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), pp.71-86.

Wang, H., Zhou, X., Sun, H., Yu, X., Zhao, J., Zhang, H. and Cui, L., 2017b. Firefly algorithm with adaptive control parameters. Soft Computing, 21(17), pp.5091-5102.

Yang, X.S., 2010, Firefly algorithms for multimodal optimization. In: International Symposium on Stochastic Algorithms. Springer, Berlin, Heidelberg, pp.169-178.

Zhao, X. and Zhang, S., 2012. Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding. EURASIP Journal on Advances in Signal Processing, 2012(1), p. 20.