DOI: https://doi.org/10.20535/SRIT.2308-8893.2020.3.05

Багатошарова МГУА-нейро-фаззі мережа на основі розширених нечітких нейронів та її використання для онлайн разпізнавання виразів обличчя

Yevgeniy V. Bodyanskiy, Yuriy P. Zaychenko, Galib Hamidov, Nonna Ye. Kulishova

Анотація


Розпізнавання зображень в реальному часі потрібно в багатьох практичних задачах. Взаємодія з користувачами в режимі онлайн потребує гнучкості та адаптацїї від прикладних програм. Метод групового урахування аргументів (МГУА) дозволяє змінювати структуру моделі та налаштовує її архітектуру відповідно до характеристик кожної задачі. Більш того, апроксимаційні властивості нео-фазі нейронів як структурних елементів системи забезпечують високу точність розпізнавання в умовах коротких вибірок даних. Запропоновано багатошарову МГУА-нейро-фаззі мережу на основі розширених нео-фаззі нейронів. Алгоритм навчання має фільтрувальні та відслідковувальні властивості та гарантує необхідну швидкість для застосувань реального часу. Ефективність запропонованої системи підтверджено в задачі розпізнавання людських емоцій.

Ключові слова


метод групового урахування аргументів; розширений нео-фаззі нейрон; онлайн розпізнавання зображень; розпізнавання виразів обличчя

Повний текст:

PDF (English)

Посилання


A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, and M.R. Wrobel, “Human-Computer Systems Interaction: Backgrounds and Applications”, ch. 3, Emotion Recognition and Its Applications. Cham: Springer International Publishing, 2014, pp. 1–62.

Kaggle. Challenges in representation learning: Facial recognition challenge, 2013.

G.U. Kharat and 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.

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, pp. 803–816, 2009.

Ch.-Yi. Lee and 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.

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.

A. Graves, J. Schmidhuber, C. Mayer, M. Wimmer, and B. Radig, “Facial Expression Recognition with Recurrent Neural Networks”, International Workshop on Cognition for Technical Systems, Munich, Germany, October 2008.

S. Ouelett, “Real-time emotion recognition for gaming using deep convolutional network features”, CoRR, vol. abs./1408.3750, 2014.

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, pp. 1–17, 2016.

A.G. Ivakhnenko, G.A. Ivakhnenko, and J.A. Mueller, “Self-organization of the neural networks with active neurons”, Pattern Recognition and Image Analysis, vol. 4, no. 2, pp. 177–18, 1994.

G.A. Ivakhnenko, “Self-organization of neuronet with active neurons for effect of nuclear test explosion forecasting”, System Analysis Modeling Simulation, vol. 20, pp. 107–116, 1995.

A.G. Ivakhnenko, D. Wuensch, and G.A. Ivakhnenko, “Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural networks”, Neural Networks, vol. 2, pp. 1169–1173, 1999.

D.U. Pham and X. Liu, Neural networks for Identification, Prediction and Control. London: Springer-Verlag, 1995, 238 p.

E. Lughofer, Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications, Berlin-Heidelberg, Springer-Verlag, 2011.

T. Ohtani, “Automatic variable selection in RBF network and its application to neurofuzzy GMDH”, Proc. Fourth Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 2, pp. 840–843, 2000.

Ye. Bodyanskiy, N. Teslenko, and P. Grimm, “Hybrid evolving neural network using kernel activation functions”, Proc. 17th Zittau East West Fuzzy Colloquium, Zittau/Goerlitz: HS, 2010, pp. 39–46.

Ye. Bodyanskiy, O. Vynokurova, and I. Pliss, “Hybrid GMDH-neural network of computational intelligence”, Proc. 3rd Int. Workshop on Inductive Modeling, Krynica, Poland, 2009, pp. 100–107.

Yu. Zaychenko, “The fuzzy Group Method of Data Handling and its application for economical processes forecasting”, Scientific Inquiry, vol. 7, no.1, pp. 83–96, 2006.

T. Ohtani, H. Ichihashi, T. Miyoshi, K. Nagasaka, and Y. Kanaumi, “Structural learning of neurofuzzy GMDH with Minkowski norm”, Proc. 1998 Second Int. Conf. on Knowledge-Based Intelligent Electronic Systems, vol. 2, pp. 100–107, 1998.

Ye. Bodyanskiy, Yu. Zaychenko, E. Pavlikovskaya, M. Samarina, and Ye. Viktorov, “The neo-fuzzy neural network structure optimization using GMDH fog the solving forecasting and classification problems”, Proc. Int. Workshop on Inductive Modeling, Krynica, Poland, 2009, pp. 77–89.

J. Miki and T. 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.

T. Yamakawa, E. Uchino, J. Miki and 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.

E. Uchino and T. 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.

Ye.V. Bodyanskiy and N.Ye. Kulishova, “Extended neo-fuzzy neuron in the task of images filtering”, Radioelectronics. Computer Science. Control, no. 1(32), pp. 112–119, 2014.

Z. Hu, Ye. Bodyanskiy, N. Kulishova, O.A. Tyshchenko, “Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition”, International Journal of Intelligent Systems and Applications (IJISA), vol. 9, no. 9, pp. 29–36, 2017.

Ye. Bodyanskiy, N. Kulishova and D. Malysheva, “The Extended Neo-Fuzzy System of Computational Intelligence and its Fast Learning for Emotions Online Recognition”, Proc. of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, August 21–25, 2018, pp.473 –478.

Ye. Bodyanskiy, N. Kulishova and O. Chala, “The Extended Multidimensional Neo-Fuzzy System and its Fast Learning in Pattern Recognition Tasks”, Data, no. 3, pp. 63–73, 2018.

S. Kaczmarz, “Angenaeherte Ausloesung von Systemen Linearer Gleichungen”, Bull. Int. Acad. Polon. Sci, Let. A, pp. 355–357, 1937.

S. Kaczmarz, “Approximate solution of systems of linear equations, Int. J. Control, vol. 53, pp. 1269–1271, 1993.

Ye. Bodyanskiy, I. Kokshenev and 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.

V. Kolodyazhniy, Ye. Bodyanskiy and P. Otto, “Universal approximator employing neo-fuzzy neurons”, Computational Intelligence: Theory and Applications, Ed. By B. Reusch, Berlin-Heidelberg: Springer, 2005, pp. 631–640.

B. Widrow and Jr.M.E. Hoff, “Adaptive switching circuits”, 1960 URE WESCON Convention Record, part 4. N.-Y.: IRE, 1960, pp. 96–104.

L.-X. Wang and J.M. Mendel, “Fuzzy basis functions, universal approximation and orthogonal least-squares learning”, IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 807–814, 1992.

L.-X. Wang, Adaptive Fuzzy Systems and Control. Design and Statistical Analysis. Upper Saddle River: Prentice Hall, 1994, 256 p.

R. J.-S. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River: Prentice Hall, 1997, 640 p.

2D face sets. Available: http://pics.psych.stir.ac.uk/2D_face_sets.htm

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.


Пристатейна бібліографія ГОСТ


1. A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, and M.R. Wrobel, “Human-Computer Systems Interaction: Backgrounds and Applications”, ch. 3, Emotion Recognition and Its Applications. Cham: Springer International Publishing, 2014, pp. 1–62.

2. Kaggle. Challenges in representation learning: Facial recognition challenge, 2013.

3. G.U. Kharat and 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, pp. 803–816, 2009.

5. Ch.-Yi. Lee and 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.

6. 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.

7. A. Graves, J. Schmidhuber, C. Mayer, M. Wimmer, and B. Radig, “Facial Expression Recognition with Recurrent Neural Networks”, International Workshop on Cognition for Technical Systems, Munich, Germany, October 2008.

8. S. Ouelett, “Real-time emotion recognition for gaming using deep convolutional network features”, CoRR, vol. abs./1408.3750, 2014.

9. 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, pp. 1–17, 2016.

10. A.G. Ivakhnenko, G.A. Ivakhnenko, and J.A. Mueller, “Self-organization of the neural networks with active neurons”, Pattern Recognition and Image Analysis, vol. 4, no. 2, pp. 177–18, 1994.

11. G.A. Ivakhnenko, “Self-organization of neuronet with active neurons for effect of nuclear test explosion forecasting”, System Analysis Modeling Simulation, vol. 20, pp. 107–116, 1995.

12. A.G. Ivakhnenko, D. Wuensch, and G.A. Ivakhnenko, “Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural networks”, Neural Networks, vol. 2, pp. 1169–1173, 1999.

13. D.U. Pham and X. Liu, Neural networks for Identification, Prediction and Control. London: Springer-Verlag, 1995, 238 p.

14. E. Lughofer, Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications, Berlin-Heidelberg, Springer-Verlag, 2011.

15. T. Ohtani, “Automatic variable selection in RBF network and its application to neurofuzzy GMDH”, Proc. Fourth Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 2, pp. 840–843, 2000.

16. Ye. Bodyanskiy, N. Teslenko, and P. Grimm, “Hybrid evolving neural network using kernel activation functions”, Proc. 17th Zittau East West Fuzzy Colloquium, Zittau/Goerlitz: HS, 2010, pp. 39–46.

17. Ye. Bodyanskiy, O. Vynokurova, and I. Pliss, “Hybrid GMDH-neural network of computational intelligence”, Proc. 3rd Int. Workshop on Inductive Modeling, Krynica, Poland, 2009, pp. 100–107.

18. Yu. Zaychenko, “The fuzzy Group Method of Data Handling and its application for economical processes forecasting”, Scientific Inquiry, vol. 7, no.1, pp. 83–96, 2006.

19. T. Ohtani, H. Ichihashi, T. Miyoshi, K. Nagasaka, and Y. Kanaumi, “Structural learning of neurofuzzy GMDH with Minkowski norm”, Proc. 1998 Second Int. Conf. on Knowledge-Based Intelligent Electronic Systems, vol. 2, pp. 100–107, 1998.

20. Ye. Bodyanskiy, Yu. Zaychenko, E. Pavlikovskaya, M. Samarina, and Ye. Viktorov, “The neo-fuzzy neural network structure optimization using GMDH fog the solving forecasting and classification problems”, Proc. Int. Workshop on Inductive Modeling, Krynica, Poland, 2009, pp. 77–89.

21. J. Miki and T. 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.

22. T. Yamakawa, E. Uchino, J. Miki and 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.

23. E. Uchino and T. 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.

24. Ye.V. Bodyanskiy and N.Ye. Kulishova, “Extended neo-fuzzy neuron in the task of images filtering”, Radioelectronics. Computer Science. Control, no. 1(32), pp. 112–119, 2014.

25. Z. Hu, Ye. Bodyanskiy, N. Kulishova, O.A. Tyshchenko, “Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition”, International Journal of Intelligent Systems and Applications (IJISA), vol. 9, no. 9, pp. 29–36, 2017.

26. Ye. Bodyanskiy, N. Kulishova and D. Malysheva, “The Extended Neo-Fuzzy System of Computational Intelligence and its Fast Learning for Emotions Online Recognition”, Proc. of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, August 21–25, 2018, pp.473 –478.

27. Ye. Bodyanskiy, N. Kulishova and O. Chala, “The Extended Multidimensional Neo-Fuzzy System and its Fast Learning in Pattern Recognition Tasks”, Data, no. 3, pp. 63–73, 2018.

28. S. Kaczmarz, “Angenaeherte Ausloesung von Systemen Linearer Gleichungen”, Bull. Int. Acad. Polon. Sci, Let. A, pp. 355–357, 1937.

29. S. Kaczmarz, “Approximate solution of systems of linear equations, Int. J. Control, vol. 53, pp. 1269–1271, 1993.

30. Ye. Bodyanskiy, I. Kokshenev and 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.

31. V. Kolodyazhniy, Ye. Bodyanskiy and P. Otto, “Universal approximator employing neo-fuzzy neurons”, Computational Intelligence: Theory and Applications, Ed. By B. Reusch, Berlin-Heidelberg: Springer, 2005, pp. 631–640.

32. B. Widrow and Jr.M.E. Hoff, “Adaptive switching circuits”, 1960 URE WESCON Convention Record, part 4. N.-Y.: IRE, 1960, pp. 96–104.

33. L.-X. Wang and J.M. Mendel, “Fuzzy basis functions, universal approximation and orthogonal least-squares learning”, IEEE Trans. on Neural Networks, vol. 3, no. 5, pp. 807–814, 1992.

34. L.-X. Wang, Adaptive Fuzzy Systems and Control. Design and Statistical Analysis. Upper Saddle River: Prentice Hall, 1994, 256 p.

35. R. J.-S. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River: Prentice Hall, 1997, 640 p.

36. 2D face sets. Available: http://pics.psych.stir.ac.uk/2D_face_sets.htm

37. 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.