RECOGNITION OF HUMAN EMOTIONS USING NEURAL NETWORKS TECHNOLOGIES
Keywords:
neural network, people facial mimicries, computer vision, python, emotions
Abstract
The analysis of existing methods of image recognition is conducted and the recognition of human mimicry as a mathematical problem is described. Much attention is given to neural network methods of facial recognition, especially the multilayer perceptron and deep convolutional neural network (DCNN). As a result of this work, a software product was created that implements the algorithm for recognizing human facial expressions using the DCNN architecture. The product is created in the language of computer programming Python using modern libraries of computer vision dlib, open cv, and the library of machine learning tensor flow from Google.Downloads
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References
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3. C. R. Darwin. The expression of the emotions in man and animals. John Murray, London, 1872.
4. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei. Image net: A large-scale hierarchical image data- base. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conf. on, pages 248{255. IEEE, 2009.
5. P. Ekman and W. V. Friesen. Constants across cul- tures in the face and emotion. Journal of personality and so- cial psychology, 17(2):124, 1971.
6. B. Fasel and J. Luettin. Automatic facial expression analysis: a survey. Pattern recognition, 36(1):259{275, 2003.
7. A. Gudi. Recognizing semantic features in faces us- ing deep learning. arXiv preprint arXiv:1512.00743, 2015.
8. Kaggle. Challenges in representation learning: Fa- cial expression recognition challenge, 2013.
9. A. Krizhevsky and G. Hinton. Learning multiple lay- ers of features from tiny images, 2009.
10. A. Krizhevsky, I. Sutskever and G. E. Hinton. Image net classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097{1105, 2012.
11. O. Langner, R. Dotsch, G. Bijlstra, D. H. Wig- boldus, S. T. Hawk and A. van Knippenberg. Presentation and validation of the radboud faces database. Cognition and emo- tion, 24(8):1377{ 1388, 2010.
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13. Y. Lv, Z. Feng and C. Xu. Facial expression recogni- tion via deep learning. In Smart Computing (SMARTCOMP), 2014 International Conference on, pages 303{308. IEEE, 2014.
14. J. Nicholson, K. Takahashi and R. Nakatsu. Emotion recognition in speech using neural net-works. Neural comput- ing & applications, 9(4): 290{296, 2000.
15. Open Source Computer Vision. Face detection using haar cascades. URL http://docs.opencv.org/master/d7/d8b/ tutorial_py_face_detection.html.
16. TFlearn. T earn: Deep learning library featuring a higher-level API for tensor ow. URL http://tflearn.org/.
Published
2017-07-14
How to Cite
KuchukН.A. Recognition of human emotions using neural networks technologies / KuchukН.A., B.G. Saatsazov // Control, Navigation and Communication Systems. Academic Journal. – Poltava: PNTU, 2017. – VOL. 4 (44). – PP. 64-69. – Available at: https://journals.nupp.edu.ua/sunz/article/view/381 (Accessed: 03.07.2024).
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
