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An Efficient Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking for Facial Expression Recognition


In this paper, an efficient facial expression recognition system using ANFIS-MHS (Adaptive Network-based Fuzzy Inference System with Mosquito Host-Seeking) has been proposed. The features were extracted using MLDA (Modified Linear Discriminant Analysis) and then the optimized parameters are computed by using mGSO (modified Glow-worm Swarm Optimization).The proposed system recognizes the facial expressions using ANFIS-MHS. The experimental results demonstrate that the proposed technique is performed better than existing classification schemes like HAKELM (Hybridization of Adaptive Kernel based Extreme Learning Machine), Support Vector Machine (SVM) and Principal Component Analysis (PCA). The proposed approach is implemented in MATLAB.



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G. Ali, M. A. Iqbal, and T. S. Choi, (2016) Boosted NNE collections for multicultural facial expression recognition, Pattern Recognition, vol. 55, pp. 14 - 27.

A. A. Amini T.E. Weymouth, and R.C. Jain, (1990) Using Dynamic Programming for solving variational problems in vision, IEEE Trans. PAMI, vol. 12, no. 9, pp. 855-867.

A. J. Calder, A. M. Burton, P. Miller, A.W. Young, and S. Akamatsu, (2001) A principal component analysis of facial expressions, Vision Research, vol. 41, no. 9, pp. 1179-1208.

I.C. Chang and C.J. Hsieh, (2011). Image Forgery Using an Enhanced Bayesian Matting Algorithm. Intelligent Automation and Soft Computing, 17(2), 269-281.

P. Lacey, J. F. Cohn, Kanade, T. Saragih, J. Ambadar, and I. Matthews, The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, IEEE, 2010, 580 pp. 94-101.

Dargazany, Aras, Ali Soleimani, and Alireza Ahmadyfard. "Multibandwidth Kernel-Based Object Tracking." Advances in Artificial Intelligence 2010 (2010): 1-15. Crossref. Web.

G. Donato, M.S. Bartlett, J. C. Hager, R. Ekman and T. J. Sejnowski, (2003) Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577.

P. Ekman, (1989) the Argument and Evidence about Universals in Facial Expressions of Emotion, John Wiley and Sons, Hoboken, NJ, USA.

X. Feng, F. C. Lau, and H. Yu, (2013) A novel bio-inspired approach based on the behavior of mosquitoes, Information Sciences, 233, 87-108.

A.P. Gosavi and S.R. Khot, (2013). Facial expression recognition using principal component analysis. International Journal of Soft Computing and Engineering (IJSCE) ISSN, 2231-2307.

Hager, G.D., and P.N. Belhumeur. "Real-Time Tracking of Image Regions with Changes in Geometry and Illumination." Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1996): n. pag. Crossref. Web.

A. P. Jacquin and A. Y. Shamseldin, (2006) Development of rainfall runoff models using Takagi-Sugeno-Kang fuzzy inference systems, J. Hydrol., 329, 154-173, 2006.

R. Jafri and H. R. Arabnia, (2009) A Survey of Face Recognition Techniques, Journal of Information Processing Systems, vol. 5, Jun.2009, pp. 41-68.

Q. Ji, Rpi intelligent systems lab (ISL) image databases, 665

Kanade, T., J.F. Cohn, and Yingli Tian. "Comprehensive Database for Facial Expression Analysis." Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) n. pag. Crossref. Web.

A. Khazaee and A. Ebrahimzadeh. (2013). Heart Arrhythmia Detection using support vector machines. Intelligent Automation and Soft Computing, 19(1), 1-9.

M. Lades, J. C. Vorbruggen, J. Buhmann, J. Lange, C. V. d. Malsburg, R. P. Wurtz, and W. Konen (1993) Distortion invariant object recognition in the dynamic link architecture, IEEE Trans. Comput., vol. 42, no. 3, pp. 300-311.

S. M. Lajevardi and Z. M. Hussain, (2012) Automatic facial expression recognition: feature extraction and selection, Signal, Image and Video Processing, vol. 6, no. 1, pp. 159-169.

H. Li, H. Ding, D. Huang, Y. Wang, X. Zhao, J. M. Morvane, and L. Chen, (2015) An efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition, Computer Vision and Image Understanding, vol. 140, pp. 83 - 92.

X. Li, (2014) Facial Expression Recognition Based on SVM, 2014 7th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 256-259, 25-26.

A. T. Lopes, E. de Aguiar, A. F. De Souza, and T. Oliveira-Santos, (2017) Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order, Pattern Recognition, 61, 610-628., 2017.

M. A. Mashrei, (2012) Neural Network and Adaptive Neuro-Fuzzy Inference System Applied to Civil Engineering Problems. In: Fuzzy Inference System, Theory and Applications, Azeem, M.F. (Ed.), ISBN- 10: 9535105251, pp: 472-490, 2012.

A. Mehrabian, (1968) Communication without words. Psychol. Today, 2, 53-56.

T. Ojala, M. Pietikäinen, and D. Harwood, (1996) A comparative study of texture measures with classification based on featured distribution, Pattern Recognition, vol. 29, no. 1, pp. 51-59.

X. Pu, K. Fan, X. Chen L. Ji, and Z. Zhou, (2015) Facial expression recognition from image sequences using twofold random forest classifier, Neuro computing, vol. 168, pp. 1173 - 1180.

R. Saini and N. Rana (2014), A Hybrid Framework of Facial Expression Recognition using SVD and PCA, International Journal of Computer Science and Information Technologies, 5(5).

G. Sandbach, S. Zafeiriou, M. Pantic, and L. Yin, (2012) Static and dynamic 3D facial expression recognition: a comprehensive survey, Image and Vision Computing, vol. 30, no. 10, pp. 683-697.

E. Sariyanidi, H. Gunes, and A. Cavallaro, (2005)Automatic analysis of facial affect: a survey of registration, representation, and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 6, pp. 1113-1133.

C. Shan, S. Gong, and P. W. McOwan, (2009) Facial expression recognition based on local binary patterns: a comprehensive study, Image and Vision Computing, vol. 27, no. 6, pp. 803-816.

M. H. Siddiqi, R. Ali, A. M. Khan, E. S. Kim, G. J. Kim, and S. Lee, (2015) Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection, Multimedia Systems, vol. 21, no. 6, pp. 541-555.

Sobia, M. Carmel, V. Brindha, and A. Abudhahir. "Facial Expression Recognition Using PCA Based Interface for Wheelchair." 2014 International Conference on Electronics and Communication Systems (ICECS) (2014): n. pag. Crossref. Web.

M. Suwa, N. Sugie, and K. Fujimora. (1978) A preliminary note on pattern recognition of human emotional expression, in Proceedings of the 4th International Joint Conference on Pattern Recognition, pp. 408-410, Kyoto, Japan, November.

Valstar, Michel F., and Maja Pantic. "Combined Support Vector Machines and Hidden Markov Models for Modeling Facial Action Temporal Dynamics." Human-Computer Interaction 118-127. Crossref. Web.

M. F. Valstar and M. Pantic, (2012) Fully automatic recognition of the temporal phases of facial actions, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 1, pp. 28-43.

J. Velagic and N. Osmic, (2013). Fuzzy-genetic identification and control structures for nonlinear helicopter model. Intelligent Automation and Soft Computing, 19(1), 51-68.

Mei Wang, Y. Iwai, and M. Yachida. "Expression Recognition from Time-Sequential Facial Images by Use of Expression Change Model." Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition n. pag. Crossref. Web.

S. Wang and Q. Ji Gan, (2017) Expression-assisted facial action unit recognition under incomplete AU annotation, Pattern Recognition, vol. 61, pp. 78 - 91.

X. H. Wang, A. Liu, and S. Q. Zhang, (2015) New facial expression recognition based on FSVM and KNN, Optik -International Journal for Light and Electron Optics, vol. 126, pp. 3132 - 3134.

Z. Wang, Q. Ruan, and G. An, (2016) Facial expression recognition using sparse local Fisher discriminant analysis, Neurocomputing, vol. 174, pp. 756 - 766.

T. Wu, M. S. Bartlett, and. J. R. Movellan, (2012) Facial expression recognition using Gabor motion energy filters, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW ”10), pp. 42-47, San Francisco, Calif, USA.

Y. Yacoob and L. S. Davis, (1996) Recognizing human facial expressions from long image sequences using optical flow, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 636-642, 1996.

H. Yan, (2016) Biased subspace learning for misalignment-robust facial expression recognition, Neurocomputing, vol. 208, pp. 202 - 209.

Ye, Fei, Zhiping Shi, and Zhongzhi Shi. "A Comparative Study of PCA, LDA and Kernel LDA for Image Classification." 2009 International Symposium on Ubiquitous Virtual Reality (2009): n. pag. Crossref. Web.

Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, (2009) A survey of affect recognition methods: audio, visual, and spontaneous expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39-58.

X. Zhang, L. Yin, J. F. Cohn, S. Canavan, M. Reale, A. Horowitz, P. Liu, and J. M. Girard, (2014) Bp4d-spontaneous: a high-resolution spontaneous 3d dynamic facial expression database, Image and Vision Computing 32 (10) 692-706.

Y. Zhang and Q. Ji, (2005) Active and dynamic information fusion for facial expression understanding from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 699-714.

W. Zhijun, L. Yuefeng J. Meng, C. Shuhan, and W. Yucun, (2015). Research on Image Retrieval of Fruit Tree Plant-Diseases and Pests Based on Nprod. Intelligent Automation and Soft Computing, 21(3), 371-381.

Y. Zhou, G. Zhou, Y. Wang, and G. Zhao (2013) A glowworm swarm optimization algorithm based tribes, Appl. Math, 7(2L), pp.537-54,


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present

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