Abstract
This paper introduces the framework based on the extraction of features using Gabor filters and modified hidden Markov model for classification. The three regions of interest (nose, mouth and eyes) are extracted using Gabor filters and dimensions are reduced by independent component analysis. Then these reduced dimensions are input to our two-layered HMM for training and testing. Seven facial expressions are recognized using publicly available JAFFE dataset. Experimental data shows the efficient and robust nature of our framework and shows its uniqueness on comparing it with other existing available methods.
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References
P. Ekman, An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)
A. Mehrabian et al., Silent Messages, vol. 8 (Wadsworth Belmont, CA, 1971)
G. Molinari, C. Bozelle, D. Cereghetti, G. Chanel, M. Bétrancourt, T. Pun, Feedback emotionnel et collaboration médiatisée par ordinateur: Quand la perception des interactions est liée aux traitsémotionnels. In Environnements Informatiques pour l’apprentissage humain, Actes de la conférence EIAH (2013), pp. 305–326
F. Ahmed, H. Bari, E. Hossain, Person-independent facial expression recognition based on compound local binary pattern (clbp). Int. Arab J. Inf. Technol. 11(2), 195–203 (2014)
G. Lei, X.-h. Li, J.-l. Zhou, X.-g. Gong, Geometric feature based facial expression recognition using multiclass support vector machines, in IEEE International Conference on Granular Computing, 2009, GRC’09 (IEEE, 2009), pp. 318–321
C. Shan, S. Gong, P.W. McOwan, Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)
F. Bashar, A. Khan, F. Ahmed, M.H. Kabir, Robust facial expression recognition based on median ternary pattern (mtp), in 2013 International Conference on Electrical Information and Communication Technology (EICT) (IEEE, 2014), pp. 1–5
T. Gritti, C. Shan, V. Jeanne, R. Braspenning, Local features based facial expression recognition with face registration errors. in 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008. FG’08, (IEEE, 2008) pp. 1–8
S.S. Meher and P. Maben, Face recognition and facial expression identification using pca, in Advance Computing Conference (IACC), 2014 IEEE International (IEEE, 2014), pp. 1093–1098
P. Carcagnì, M. Coco, M. Leo, C. Distante, Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4(1), 1 (2015)
A.A. Gunawan et al., Face expression detection on kinect using active appearance model and fuzzy logic. Procedia Comput. Sci. 59, 268–274 (2015)
R. Shbib, S. Zhou, Facial expression analysis using active shape model. Int. J. Sig. Process. Image Process. Pattern Recognit. 8(1), 9–22 (2015)
J. de Andrade Fernandes, L.N. Matos, M.G. dos Santos Arag˜ao, Geometrical approaches for facial expression recognition using support vector machines, in Conference on Graphics, Patterns and Images (SIBGRAPI), 2016 29th SIBGRAPI (IEEE, 2016), pp. 347–354
L. Wang, R. Li, K. Wang, A novel automatic facial expression recognition method based on AAM. J. Comput. 9(3), 608–617 (2014)
J. Chen, Z. Chen, Z. Chi, H. Fu, Facial expression recognition based on facial components detection and hog features, in International Workshops on Electrical and Computer Engineering Subfields (2014), pp. 884–888
A.R. Rivera, J.R. Castillo, O.O. Chae, Local directional number pattern for face analysis: face and expression recognition. IEEE Trans. Image Process. 22, 1740–1752 (2013). https://doi.org/10.1109/TIP.2012.2235848
C. Li, A. Soares, Automatic facial expression recognition using 3D faces. Int. J. Eng. Res. Innov. 3, 30–34 (2011)
C. Shan, S. Gong, P.W. McOwan, Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27, 803–816 (2009). https://doi.org/10.1016/j.imavis.2008.08.005
S. Jain, C. Hu, J.K. Aggarwal, Facial expression recognition with temporal modeling of shapes, in Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain. 6–13 Nov 2011 (IEEE Piscataway, NJ, USA, 2011), pp. 1642–1649
T. Wu, M.S. Bartlett, J.R. Movellan, Facial expression recognition using Gabor motion energy filters, in Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, CA, USA, 13–18 June 2010, (IEEE, Piscataway, NJ, USA, 2010), pp. 42–47
L. Zhang, M. Jiang, D. Farid, M.A. Hossain, Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot. Expert Syst. Appl. 40, 5160–5168 (2013). https://doi.org/10.1016/j.eswa.2013.03.016
Acknowledgements
Japanese Female Facial Expression (JAFFE) is publicly available datasets. It is available free of charge from Web site http://www.kasrl.org/jaffe.html. The database was planned and assembled by Michael Lyons, Miyuki Kamachi and Jiro Gyoba.
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Rahul, M., Shukla, R., Goyal, P.K., Siddiqui, Z.A., Yadav, V. (2021). Gabor Filter and ICA-Based Facial Expression Recognition Using Two-Layered Hidden Markov Model. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_42
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DOI: https://doi.org/10.1007/978-981-15-1275-9_42
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