Метод нейромережевого розпізнавання емоцій по зображенню обличчя.
Ключові слова:
розпізнавання емоцій, інформаційно-управляюча система, зображення обличчя, нейронна мережа, захист інформації, продукційне правило.
Анотація
Стаття присвячена вирішенню задачі вдосконалення нейромережевих засобів розпізнавання емоцій операторів інформаційно-управляючих систем на основі зображення обличчя. З’ясовано, що труднощі розробки таких засобів пов’язані з формуванням репрезентативної навчальної вибірки. Запропоновано нівелювати означені труднощі за рахунок застосування експертних знань. Розроблено метод нейромережевого розпізнавання емоцій по зображенню обличчя людини, який за рахунок запропонованого підходу до застосування продукційних правил для подання в нейронну мережу експертних знань, дозволяє підвищити оперативність розпізнавання та розширити множину видів складних емоцій, характеристики яких не представлені в статистичних даних. Проведені експериментальні дослідження показали, що використання розробленого методу дозволяє забезпечити помилку розпізнавання емоцій на рівні кращих сучасних систем подібного призначення.
Посилання
Akhmetov B., Tereikovskyi I., Tereikovska L., Adranova A. Neural Network User Authentication by Geometry of the Auricle. Recent Developments in Data Science and Intelligent Analysis of Information Proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information, June 4–7, 2018, Kyiv, Ukraine, pp.11-19.
Akhmetov B., Tereykovsky I. , Doszhanova A., Tereykovskaya L. (2018). Determination of input parameters of the neural network model, intended for phoneme recognition of a voice signal in the systems of distance learning. International Journal of Electronics and Telecommunications. Vol 64, No 4 (2018), pp. 425-432.
Anderson K., McOwan Peter W. A realtime automated system for the recognition of human facial expressions // Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on , vol.36, no.1, pp.96-105, 2006.
Batista J., Albiero V., Bellon O., Silva L. Aumpnet: simultaneous action units detection and intensity estimation on multipose facial images using a single convolutional neural network. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 866–871. IEEE, 2017.
Chandrani S., Washef A., Soma M., Debasis M. Facial Expressions: A Cross-Cultural Study. Emotion Recognition: A Pattern Analysis Approach. Wiley Publ., 2015, pp. 69–86.
Dychka I., Tereikovskyi I., Tereikovska L., Pogorelov V., Mussiraliyeva S. (2018). Deobfuscation of computer virus malware code with value state dependence graph. Advances in Intelligent Systems and Computing, pp. 370-379, 2018.
Ertugrul O., Jeni L., Cohn J. FACSCaps: Pose-Independent Facial Action Coding with Capsules. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, 2018, pp. 2211-221109.
Ghosh S., Laksana E., Scherer S., Morency L.-P. A multi-label convolutional neural network approach to crossdomain action unit detection. In Affective Computing and Intelligent Interaction, 2015 International Conference on, pages 609–615. IEEE, 2015.
Goodfellow J., Erhan D., Carrier L. (2015). Challenges in representation learning: A report on three machine learning contests, Neural Networks 2015, vol. 64, pp. 59–63.
Gudi A., Tasli H., Den Uyl T., Maroulis A. Deep learning based facs action unit occurrence and intensity estimation. In Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on, vol. 6, pp. 1–5.
He J., Li D., Yang B., Cao S., Sun B., Yu L.. Multi view facial action unit detection based on cnn and blstmrnn. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 848–853. IEEE, 2017.
Hu Z., Tereykovskiy I., Tereykovska L., Zhibek A. Optimization of convolutional neural network structure for biometric authentication by face geometry. Advances in Intelligent Systems and Computing. 2018. Volume 754, pp 567-577.
Ilbeygi M., Shah-Hosseini H. A novel fuzzy facial expression recognition system based on facial feature extraction from color face images. Engineering Applications of Artificial Intelligence 25, 2012, pp. 130–146.
Erik L., Gary B., RoyChowdhury A., Haoxiang L. Labeled Faces in the Wild: A Survey. In Advances in Face Detection and Facial Image Analysis, Springer, pages 189-248, 2016.
Seyed M., Marjan A., Face emotion recognition system based on fuzzy logic using algorithm improved Particle Swarm, International Journal of Computer Science and Network Security, V. 16 No.7, July 2016, pp. 157-166.
Tariq U., Lin K., Li Z., Zhou Z., Wang Z., Le V., Huang T.S., Lv X., Han T.X. Emotion Recognition from an Ensemble of Features. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, 2012, vol. 42 (4), pp. 1017–1026.
Tereikovska L., Tereikovskyi I., Mussiraliyeva S., Akhmed G. Recognition of emotions by facial Geometry using a capsule neural network. International Journal of Civil Engineering and Technology (IJCIET). Vol. 10, Issue 04, April 2019, pp. 270-279
Tereykovska L., Tereykovskiy I., Aytkhozhaeva E., Tynymbayev S., Imanbayev A. (2017). Encoding of neural network model exit signal, that is devoted for distinction of graphical images in biometric authenticate systems. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, Volume 6, Number 426, pp. 217 – 224, 2017.
Tereykovskaya L., Petrov O., Aleksander M. Prospects of neural networks in business models. TransComp 2015. 30 November – 3 December, 2015, Zakopanem, Poland. – P. 1539–1545.
Toliupa S., Tereikovskiy I., Dychka I., Tereikovska L., Trush A. The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry. 3rd International Conference on Advanced Information and Communications Technologies (AICT). 2019, 2-6 July 2019, Lviv, Ukraine, Page(s): 323 - 327
21. Toser Z., Jeni L., Lorincz A., Cohn F. Deep learning for facial action unit detection under large head poses. In European Conference on Computer Vision, pages 359–371. Springer, 2016.
22. Wang Z., Li Y., Wang S., Ji Q.. Capturing global semantic relationships for facial action unit recognition. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 3304–3311. IEEE, 2013.
23. Zhao K., Chu W., Zhang H.. Deep region and multilabel learning for facial action unit detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 3391-3399.
Akhmetov B., Tereykovsky I. , Doszhanova A., Tereykovskaya L. (2018). Determination of input parameters of the neural network model, intended for phoneme recognition of a voice signal in the systems of distance learning. International Journal of Electronics and Telecommunications. Vol 64, No 4 (2018), pp. 425-432.
Anderson K., McOwan Peter W. A realtime automated system for the recognition of human facial expressions // Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on , vol.36, no.1, pp.96-105, 2006.
Batista J., Albiero V., Bellon O., Silva L. Aumpnet: simultaneous action units detection and intensity estimation on multipose facial images using a single convolutional neural network. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 866–871. IEEE, 2017.
Chandrani S., Washef A., Soma M., Debasis M. Facial Expressions: A Cross-Cultural Study. Emotion Recognition: A Pattern Analysis Approach. Wiley Publ., 2015, pp. 69–86.
Dychka I., Tereikovskyi I., Tereikovska L., Pogorelov V., Mussiraliyeva S. (2018). Deobfuscation of computer virus malware code with value state dependence graph. Advances in Intelligent Systems and Computing, pp. 370-379, 2018.
Ertugrul O., Jeni L., Cohn J. FACSCaps: Pose-Independent Facial Action Coding with Capsules. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, 2018, pp. 2211-221109.
Ghosh S., Laksana E., Scherer S., Morency L.-P. A multi-label convolutional neural network approach to crossdomain action unit detection. In Affective Computing and Intelligent Interaction, 2015 International Conference on, pages 609–615. IEEE, 2015.
Goodfellow J., Erhan D., Carrier L. (2015). Challenges in representation learning: A report on three machine learning contests, Neural Networks 2015, vol. 64, pp. 59–63.
Gudi A., Tasli H., Den Uyl T., Maroulis A. Deep learning based facs action unit occurrence and intensity estimation. In Automatic Face and Gesture Recognition (FG), 2015 11th IEEE International Conference and Workshops on, vol. 6, pp. 1–5.
He J., Li D., Yang B., Cao S., Sun B., Yu L.. Multi view facial action unit detection based on cnn and blstmrnn. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 848–853. IEEE, 2017.
Hu Z., Tereykovskiy I., Tereykovska L., Zhibek A. Optimization of convolutional neural network structure for biometric authentication by face geometry. Advances in Intelligent Systems and Computing. 2018. Volume 754, pp 567-577.
Ilbeygi M., Shah-Hosseini H. A novel fuzzy facial expression recognition system based on facial feature extraction from color face images. Engineering Applications of Artificial Intelligence 25, 2012, pp. 130–146.
Erik L., Gary B., RoyChowdhury A., Haoxiang L. Labeled Faces in the Wild: A Survey. In Advances in Face Detection and Facial Image Analysis, Springer, pages 189-248, 2016.
Seyed M., Marjan A., Face emotion recognition system based on fuzzy logic using algorithm improved Particle Swarm, International Journal of Computer Science and Network Security, V. 16 No.7, July 2016, pp. 157-166.
Tariq U., Lin K., Li Z., Zhou Z., Wang Z., Le V., Huang T.S., Lv X., Han T.X. Emotion Recognition from an Ensemble of Features. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, 2012, vol. 42 (4), pp. 1017–1026.
Tereikovska L., Tereikovskyi I., Mussiraliyeva S., Akhmed G. Recognition of emotions by facial Geometry using a capsule neural network. International Journal of Civil Engineering and Technology (IJCIET). Vol. 10, Issue 04, April 2019, pp. 270-279
Tereykovska L., Tereykovskiy I., Aytkhozhaeva E., Tynymbayev S., Imanbayev A. (2017). Encoding of neural network model exit signal, that is devoted for distinction of graphical images in biometric authenticate systems. News of the National Academy of Sciences of the Republic of Kazakhstan, Series of Geology and Technical Sciences, Volume 6, Number 426, pp. 217 – 224, 2017.
Tereykovskaya L., Petrov O., Aleksander M. Prospects of neural networks in business models. TransComp 2015. 30 November – 3 December, 2015, Zakopanem, Poland. – P. 1539–1545.
Toliupa S., Tereikovskiy I., Dychka I., Tereikovska L., Trush A. The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry. 3rd International Conference on Advanced Information and Communications Technologies (AICT). 2019, 2-6 July 2019, Lviv, Ukraine, Page(s): 323 - 327
21. Toser Z., Jeni L., Lorincz A., Cohn F. Deep learning for facial action unit detection under large head poses. In European Conference on Computer Vision, pages 359–371. Springer, 2016.
22. Wang Z., Li Y., Wang S., Ji Q.. Capturing global semantic relationships for facial action unit recognition. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 3304–3311. IEEE, 2013.
23. Zhao K., Chu W., Zhang H.. Deep region and multilabel learning for facial action unit detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 3391-3399.
Опубліковано
2020-09-24
Як цитувати
Терейковська, Л. (2020). Метод нейромережевого розпізнавання емоцій по зображенню обличчя. КОМП’ЮТЕРНО-ІНТЕГРОВАНІ ТЕХНОЛОГІЇ: ОСВІТА, НАУКА, ВИРОБНИЦТВО, (40), 146-152. https://doi.org/10.36910/6775-2524-0560-2020-40-22
Розділ
Інформатика та обчислювальна техніка
