https://oldena.lpnu.ua/handle/ntb/52550| Title: | The Multidimensional Extended Neo-Fuzzy System and its Fast Learning for Emotions Online Recognition |
| Authors: | Bodyanskiy, Yevgeniy Kulishova, Nonna Malysheva, Daria |
| Affiliation: | Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Bodyanskiy Y. The Multidimensional Extended Neo-Fuzzy System and its Fast Learning for Emotions Online Recognition / Yevgeniy Bodyanskiy, Nonna Kulishova, Daria Malysheva // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 473–477. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Bodyanskiy Y. The Multidimensional Extended Neo-Fuzzy System and its Fast Learning for Emotions Online Recognition / Yevgeniy Bodyanskiy, Nonna Kulishova, Daria Malysheva // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 473–477. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | extended neo-fuzzy system online emotion recognition entropy-information learning criterion |
| Number of pages: | 5 |
| Page range: | 473-477 |
| Start page: | 473 |
| End page: | 477 |
| Abstract: | Many tasks require human facial expressions automatic recognition in real time. Recent solutions to this problem using machine learning methods have been based on the applying of training data sets that include hundreds of thousands of samples. The formation of these data is too costly. In this paper, the architecture of a system using extended neofuzzy neurons for online emotions recognition is examined. We propose the algorithm which is based on the entropy criterion for learning the system and reducing the amount of training data thousands of times. |
| URI: | https://ena.lpnu.ua/handle/ntb/52550 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://pics.psych.stir.ac.uk/2D_face_sets.htm |
| References (Ukraine): | [1] A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, M. R. Wrobel, “Human-Computer Systems Interaction: Backgrounds and Applications,” ch. 3, Emotion Recognition and Its Applications. Cham: Springer International Publishing, 2014, pp. 51 – 62. [2] Kaggle. Challenges in representation learning: Facial recognition challenge, 2013. [3] G.U. Kharat, S.V. Dudul, “Emotion Recognition from Facial Expression Using Neural Networks,” in Human-Computer Systems Interaction. Advances in Intelligent and Soft Computing, vol 60, Z.S. Hippe, J.L. Kulikowski, Eds. Berlin, Heidelberg: Springer, 2009. [4] C. Shan, S. Gong, and P. W. McOwan, “Facial expression recognition based on local binary patterns: A comprehensive study,” Image and Vision Computing, vol. 27, no. 6, 2009, pp. 803 – 816. [5] B. Fazel, J. Luettin, “Automatic facial expression analysis: a survey”, Pattern Recognition, 36(1), 2003, pp. 259 – 275. [6] Ch.-Yi Lee, Li-Ch. Liao, “Recognition of Facial Expression by Using Neural-Network System with Fuzzified Characteristic Distances Weights,” IEEE Int. Conf. Fuzzy Systems FUZZ-IEEE 2008. [IEEE World Congress on Computational Intelligence, pp. 1694 – 1699, 2008]. [7] N. Kulishova, “Emotion Recognition Using Sigma-Pi Neural Network,” Proc. of 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 2016, pp. 327 –331. [8] A. Graves, J. Schmidhuber, C. Mayer, M. Wimmer, B. Radig, “Facial Expression Recognition with Recurrent Neural Networks,” International Workshop on Cognition for Technical Systems, Munich, Germany, October 2008. [9] S. Ouelett, “Real-time emotion recognition for gaming using deep convolutional network features,” CoRR, vol. abs./1408.3750, 2014. [10] B. Kim, J. Roh, S. Dong, and S. Lee, “Hierarchical committee of deep convolutional neural networks for robust facial expression recognition,” Journal on Multimodal User Interfaces, 2016, pp. 1–17. [11] J. Miki, J. Yamakawa, “Analog implementation of neo-fuzzy neuron and its on-board learning,” in Computational Intelligence and Applications, Ed. N.E. Mastorakis, Piraeus: WSES Press, 1999, pp. 144 – 149. [12] J. Yamakawa, E. Uchino, J. Miki, H. Kusanagi, “A neo-fuzzy neuron and its application to system identification and prediction of the system behavior,” Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks “IIZUKA-92”, Iizuka, Japan, 1992, pp. 477 – 483. [13] E. Uchino, J. Yamakawa, “Soft computing based signal prediction, restoration and filtering,” in Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algoritms, Ed. Da Ruan, Boston: Kluwer Academic Publishers, 1997, pp. 331 – 349. [14] Ye.V. Bodyanskiy, N.Ye. Kulishova, “Extended neo-fuzzy neuron in the task of images filtering,” Radioelectronics. Computer Science. Control, № 1(32), 2014, pp. 112 – 119. [15] Ye. Bodyanskiy, Y. Victorov, “The cascade of neo-fuzzy architecture and its online learning algorithm,” Int. Book Series Inf. Sci. Comput., 17(1), 2010, pp. 110 – 116. [16] Ye. Bodyanskiy, I. Kokshenev, V. Kolodyazhniy, “An adaptive learning algorithm for a neo-fuzzy neuron,” Proc. of the 3rd Conference of the European Society for Fuzzy Logic and Technology, pp. 375 – 379, 2005. [17] D. Zurita, M. Delgado, J.A. Carino, J.A. Ortega, G. Clerc, “Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron,” IEEE Access, vol. 4, 2016, pp. 6151 – 6160. [18] M. Pandit, L. Srivastava, V. Singh, “On-line voltage security assessment using modified neo-fuzzy neuron based classifier,” IEEE Int. Conf. Ind. Technol., 2006, pp. 899 – 904. [19] H.D. Kim, “Optimal learning of neo-fuzzy structure using bacteria foraging optimisation,” Proceedings of the ICCA, 2005. [20] A.M. Silva, W. Caminhas, A. Lemos, F. Gomide, “A fast learning algorithm for evolving neo-fuzzy neuron,” Applied Soft Computing, vol. 14, Part B, January 2014, pp. 194 – 209. [21] T. Takagi, M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. On System, Man and Cybernetics, 15, 1985, pp. 116 – 132. [22] J.-S. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River: Prentice Hall, 1997. [23] Z. Hu, Ye.V. Bodyanskiy, N.Ye. Kulishova, O.K. Tyshchenko, “A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition,” International Journal of Intelligent Systems and Applications (IJISA), vol.9, No.9, 2017, pp.29 – 36. [24] G.C. Goodwin, P.J. Ramage, P.E. Caines, “Discrete time stochastic adaptive control,” SIAM J. Control and Optimisation, 19, 1981, pp. 829– 853. [25] S. Haykin, Neural Networks. A Comprehensive Foundation. Upper Saddle River: Prentice Hall, 1999. [26] A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing. Stuttgart: Teubner, 1993. [27] http://pics.psych.stir.ac.uk/2D_face_sets.htm [28] P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression," Proceedings of IEEE workshop on CVPR for Human Communicative Behavior Analysis, San Francisco, USA, 2010. |
| References (International): | [1] A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, M. R. Wrobel, "Human-Computer Systems Interaction: Backgrounds and Applications," ch. 3, Emotion Recognition and Its Applications. Cham: Springer International Publishing, 2014, pp. 51 – 62. [2] Kaggle. Challenges in representation learning: Facial recognition challenge, 2013. [3] G.U. Kharat, S.V. Dudul, "Emotion Recognition from Facial Expression Using Neural Networks," in Human-Computer Systems Interaction. Advances in Intelligent and Soft Computing, vol 60, Z.S. Hippe, J.L. Kulikowski, Eds. Berlin, Heidelberg: Springer, 2009. [4] C. Shan, S. Gong, and P. W. McOwan, "Facial expression recognition based on local binary patterns: A comprehensive study," Image and Vision Computing, vol. 27, no. 6, 2009, pp. 803 – 816. [5] B. Fazel, J. Luettin, "Automatic facial expression analysis: a survey", Pattern Recognition, 36(1), 2003, pp. 259 – 275. [6] Ch.-Yi Lee, Li-Ch. Liao, "Recognition of Facial Expression by Using Neural-Network System with Fuzzified Characteristic Distances Weights," IEEE Int. Conf. Fuzzy Systems FUZZ-IEEE 2008. [IEEE World Congress on Computational Intelligence, pp. 1694 – 1699, 2008]. [7] N. Kulishova, "Emotion Recognition Using Sigma-Pi Neural Network," Proc. of 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, 2016, pp. 327 –331. [8] A. Graves, J. Schmidhuber, C. Mayer, M. Wimmer, B. Radig, "Facial Expression Recognition with Recurrent Neural Networks," International Workshop on Cognition for Technical Systems, Munich, Germany, October 2008. [9] S. Ouelett, "Real-time emotion recognition for gaming using deep convolutional network features," CoRR, vol. abs./1408.3750, 2014. [10] B. Kim, J. Roh, S. Dong, and S. Lee, "Hierarchical committee of deep convolutional neural networks for robust facial expression recognition," Journal on Multimodal User Interfaces, 2016, pp. 1–17. [11] J. Miki, J. Yamakawa, "Analog implementation of neo-fuzzy neuron and its on-board learning," in Computational Intelligence and Applications, Ed. N.E. Mastorakis, Piraeus: WSES Press, 1999, pp. 144 – 149. [12] J. Yamakawa, E. Uchino, J. Miki, H. Kusanagi, "A neo-fuzzy neuron and its application to system identification and prediction of the system behavior," Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks "IIZUKA-92", Iizuka, Japan, 1992, pp. 477 – 483. [13] E. Uchino, J. Yamakawa, "Soft computing based signal prediction, restoration and filtering," in Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algoritms, Ed. Da Ruan, Boston: Kluwer Academic Publishers, 1997, pp. 331 – 349. [14] Ye.V. Bodyanskiy, N.Ye. Kulishova, "Extended neo-fuzzy neuron in the task of images filtering," Radioelectronics. Computer Science. Control, No 1(32), 2014, pp. 112 – 119. [15] Ye. Bodyanskiy, Y. Victorov, "The cascade of neo-fuzzy architecture and its online learning algorithm," Int. Book Series Inf. Sci. Comput., 17(1), 2010, pp. 110 – 116. [16] Ye. Bodyanskiy, I. Kokshenev, V. Kolodyazhniy, "An adaptive learning algorithm for a neo-fuzzy neuron," Proc. of the 3rd Conference of the European Society for Fuzzy Logic and Technology, pp. 375 – 379, 2005. [17] D. Zurita, M. Delgado, J.A. Carino, J.A. Ortega, G. Clerc, "Industrial Time Series Modelling by Means of the Neo-Fuzzy Neuron," IEEE Access, vol. 4, 2016, pp. 6151 – 6160. [18] M. Pandit, L. Srivastava, V. Singh, "On-line voltage security assessment using modified neo-fuzzy neuron based classifier," IEEE Int. Conf. Ind. Technol., 2006, pp. 899 – 904. [19] H.D. Kim, "Optimal learning of neo-fuzzy structure using bacteria foraging optimisation," Proceedings of the ICCA, 2005. [20] A.M. Silva, W. Caminhas, A. Lemos, F. Gomide, "A fast learning algorithm for evolving neo-fuzzy neuron," Applied Soft Computing, vol. 14, Part B, January 2014, pp. 194 – 209. [21] T. Takagi, M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. On System, Man and Cybernetics, 15, 1985, pp. 116 – 132. [22] J.-S. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River: Prentice Hall, 1997. [23] Z. Hu, Ye.V. Bodyanskiy, N.Ye. Kulishova, O.K. Tyshchenko, "A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition," International Journal of Intelligent Systems and Applications (IJISA), vol.9, No.9, 2017, pp.29 – 36. [24] G.C. Goodwin, P.J. Ramage, P.E. Caines, "Discrete time stochastic adaptive control," SIAM J. Control and Optimisation, 19, 1981, pp. 829– 853. [25] S. Haykin, Neural Networks. A Comprehensive Foundation. Upper Saddle River: Prentice Hall, 1999. [26] A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing. Stuttgart: Teubner, 1993. [27] http://pics.psych.stir.ac.uk/2D_face_sets.htm [28] P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression," Proceedings of IEEE workshop on CVPR for Human Communicative Behavior Analysis, San Francisco, USA, 2010. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
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| 2018_Bodyanskiy_Y-The_Multidimensional_473-477.pdf | 352.4 kB | Adobe PDF | View/Open | |
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