Information synthesis of adaptive system for visual diagnostics of emotional and mental state of a person
DOI:
https://doi.org/10.15587/1729-4061.2016.75683Keywords:
optimization, information-extreme intelligent technology, information criterion, psychodiagnostics, training, criteria of functional efficiencyAbstract
The visual method of the recognition of emotional and mental state of a person by his face images was proposed. An input mathematical description of psychodiagnostic system was formed according to the results of the analysis of the left and right hemisphere images of the face of a person. Information synthesis of the system was carried out in the framework of the information-extreme intelligence technologies of data analysis, which is based on maximizing the performance of machine learning. In this case, the effect of the RGB-components of color images on the functional efficiency of machine training of psychodiagnostic system was studied. The proposed method, unlike the existing methods, allows improving the accuracy of psychodiagnostics through the application of the developed modification of entropic criterion, capable to capture the smallest changes in the face images, which occur under the influence of external exciting factors. By the results of the physical simulation it was proved that the RGB-components of a color image of the face of person affect differently the information capability of the system. It was established that the exclusion from the input matrix of the recognition features for the red component of the image improves functional efficiency of psychodiagnostic system. In addition, the exclusion from the RGB-spectrum of the blue and green components do not change the value of information criterion, but they cannot be excluded from this spectrum, since they become informative in the complex. The obtained scientific results are of great practical importance for determining emotional and mental state of a person, for example, when evaluating his suitability for a profession, ability to perform his functional duties, especially under extreme conditions, etc. Besides, the obtained results allow assessing functional efficiency of correction of emotional and mental state of a patient.
References
- Wang, J.-W., Le, N. T., Lee, J.-S., Wang, C.-C. (2016). Color face image enhancement using adaptive singular value decomposition in fourier domain for face recognition. Pattern Recognition, 57, 31–49. doi: 10.1016/j.patcog.2016.03.021
- Pattanasethanon, P., Savithi,C. (2012). Human Face Detection and Recognition using Web-Cam. Journal of Computer Science, 8(9), 1585–1593. doi: 10.3844/jcssp.2012.1585.1593
- Magesh, K.C., Ragul, G., Thiyagarajan, R. (2013). Gabor featured statistical modeling in face recognition with chaotic database. International Journal of Emerging Technology and Advanced Engineering, 3 (S1), 278–284.
- Campomanes-Álvarez, B. R., Ibáñez, O., Navarro, F., Alemán, I., Botella, M., Damas, S., Cordón, O. (2014). Computer vision and soft computing for automatic skull–face overlay in craniofacial superimposition. Forensic Science International, 245, 77–86. doi: 10.1016/j.forsciint.2014.10.009
- Takehara, T., Ochiai, F., Suzuki, N. (2015). A small-world network model of facial emotion recognition. The Quarterly Journal of Experimental Psychology, 69(8), 1508–1529. doi: 10.1080/17470218.2015.1086393
- De Brito Ferreira Fernandes, F., Gigante, A. D., Berutti, M., Amaral, J. A., de Almeida, K. M., de Almeida Rocca, C. C. et. al. (2016). Facial emotion recognition in euthymic patients with bipolar disorder and their unaffected first-degree relatives. Comprehensive Psychiatry, 68, 18–23. doi: 10.1016/j.comppsych.2016.03.001
- Ali Khan, S., Hussain, A., Usman, M. (2016). Facial expression recognition on real world face images using intelligent techniques: A survey. Optik - International Journal for Light and Electron Optics, 127(15), 6195–6203. doi: 10.1016/j.ijleo.2016.04.015
- Xiaofeng, F. (2009). Facial expression recognition based on multi-scale centralized binary pattern. Control Theory & Applications, 6, 26–32.
- Anbarjafari, G. (2013). Face recognition using color local binary pattern from mutually independent color channels. EURASIP Journal on Image and Video Processing, 2013(1), 6. doi: 10.1186/1687-5281-2013-6
- Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., Gorno-Tempini, M. L. (2014). Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse. Cortex, 55, 122–129. doi: 10.1016/j.cortex.2013.05.008
- Dovbysh, A. S. (2009). Foundation of intelligent systems designing: tutorial. Sumy: Publisher SSU, 171.
- Shelehov, І. V., Prylepa, D. V. (2014). Optimization of the parameters of learning computerized diagnostic systems emotional-mental state of the person. Radioelectronic and computer systems, 1(65), 161–167.
- Dovbysh, A. S., Shelehov, І. V., Prylepa, D. V. (2014). Information-extreme algorithm for system diagnostics emotional and mental person’s state learning. Radio Electronics, Computer Science, Control, 2(31), 156–163.
- Anuashvili, A. N. (2008). Objective psychology based on the wave model of the brain. Moscow: Econ-Inform, 292.
- Khatuntsev, A. Y., Martynova, N. S. (2009). Computational Geometry and Computer Graphics. Sumy: Publisher SSU, 137.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 Anatoliy Dovbysh, Igor Shelehov, Dmitriy Prylepa, Ivan Golub
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
