Context. The article is devoted to the problem of a training data set forming for the automatic human emotions recognition<br />system on the basis of a multidimensional extended neo-fuzzy neuron. The aspects of choice the attributes vector’s dimension and<br />composition, their influence on the system learning rate are considered. The object of research is the method of multidimensional<br />data clustering. The subject of research is two-dimensional images geometric features systematization.<br />Objective. The main goal of the work is to develop an approach to person’s face expression description using geometric features<br />fixed set that can be obtained by video sequence frames processing.<br />Method. To study the facial expressions recognition system it is proposed to form a feature vector consisting of characteristic<br />points coordinates. There were selected points that relate to the location and shape of the eyelids, eyebrows, eye pupils, lips contours,<br />nose wings, nasolabial folds. Such points can be easily found during the automatic image processing using known contour detectors.<br />Also, the possibility of using for the human facial expression description not the coordinates of characteristic points, but the distances<br />between them, was investigated. From these distances a different feature vector was created, the properties of which were compared<br />with the points coordinates vector.<br />Results. The developed recognition system on the basis of a multidimensional extended neo-fuzzy neuron have been<br />implemented in software and investigated for solving the problem of facial expression classification. A comparison between the<br />attribute vectors that are different in composition and dimension is made. The structure for the feature vector, which provides high<br />system learning rate, and does not require the additional structural elements was chosen.<br />Conclusions. The experimental study fully confirms the effectiveness of the developed approach for the human facial<br />expressions recognition using a multidimensional extended neo-fuzzy neuron.
Науковий журнал «Радіоелектроніка, інформатика, управління»
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Bodyanskiy, Ye. V.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine Kulishova, N. Ye.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine Tkachenko, V. Ph.; Kharkiv National University of Radioelectronics, Kharkiv, Ukraine |
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Context. The article is devoted to the problem of a training data set forming for the automatic human emotions recognition<br />system on the basis of a multidimensional extended neo-fuzzy neuron. The aspects of choice the attributes vector’s dimension and<br />composition, their influence on the system learning rate are considered. The object of research is the method of multidimensional<br />data clustering. The subject of research is two-dimensional images geometric features systematization.<br />Objective. The main goal of the work is to develop an approach to person’s face expression description using geometric features<br />fixed set that can be obtained by video sequence frames processing.<br />Method. To study the facial expressions recognition system it is proposed to form a feature vector consisting of characteristic<br />points coordinates. There were selected points that relate to the location and shape of the eyelids, eyebrows, eye pupils, lips contours,<br />nose wings, nasolabial folds. Such points can be easily found during the automatic image processing using known contour detectors.<br />Also, the possibility of using for the human facial expression description not the coordinates of characteristic points, but the distances<br />between them, was investigated. From these distances a different feature vector was created, the properties of which were compared<br />with the points coordinates vector.<br />Results. The developed recognition system on the basis of a multidimensional extended neo-fuzzy neuron have been<br />implemented in software and investigated for solving the problem of facial expression classification. A comparison between the<br />attribute vectors that are different in composition and dimension is made. The structure for the feature vector, which provides high<br />system learning rate, and does not require the additional structural elements was chosen.<br />Conclusions. The experimental study fully confirms the effectiveness of the developed approach for the human facial<br />expressions recognition using a multidimensional extended neo-fuzzy neuron. |
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Zaporizhzhya National Technical University 2018-12-07 16:07:43 |
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application/pdf http://ric.zntu.edu.ua/article/view/149648 |
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Radio Electronics, Computer Science, Control; No 3 (2018): Radio Electronics, Computer Science, Control |
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Copyright (c) 2018 Ye. V. Bodyanskiy, N. Ye. Kulishova, V. Ph. Tkachenko |
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