Інформаційна технологія визначення характерних ознак на обличчі для розпізнавання емоційних проявів
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Дата
2020
Автори
Іващенко, C.О.
Калита, О.Д.
Бармак, О.В.
Скрипник, Т.К.
Ivaschenko, S.
Kalyta, O.
Barmak, O.
Skrypnyk, T.
Назва журналу
Номер ISSN
Назва тому
Видавець
Хмельницький національний університет
Анотація
У роботі наведено та експериментально досліджено технологію визначення характерних ознак на обличчі для
розпізнавання емоційних проявів. Наведені кроки технології дозволяють отримати достатньо простим, обчислювально
нескладним способом модель подання емоційних станів на обличчі за допомогою векторів характерних ознак згрупованих за
класами емоцій та визначити коефіцієнти гіперплощин, які опукло обмежують ці класи і можуть бути використанні для
наступного розпізнавання довільного емоційного виразу обличчя.
One of the ways to process an image presented in the form of a set of pixels, in order to further identify, classify the objects present on it is to display the specified set in the form of sets of certain features. Such features are not universal in nature, but rather significantly depend on the tasks under consideration. For certain classes of problems, such features (model) are selected that best allow the application of appropriate methods to solve the problem. The paper considers a class of problems for recognizing the emotional state on a person's face. In, a convolutional neural network (CNN) is used to detect emotions. CNN differs from multilayer perceptron (MLP) in that they have hidden layers called convolutional layers. The proposed method is based on a two-tier CNN system. At the first level, the background of the image is removed to better reflect emotions. A standard CNN network module is used to obtain the primary expression vector (EV). EV is formed by tracking the relevant important points of the face. EV is directly related to changes in facial expression. EV is obtained using a basic perceptron unit plotted on a face image with the background removed. In the proposed model at the last stage there is a nonconvolutionary perceptron layer. Each of the convoluted layers receives input data (images), converts them, and then takes them to the next level. After detecting a face, the CNN filter of the second part captures parts of the face, such as eyes, ears, lips, nose and cheeks. The authors agree that the method has some limitations, and especially requires high computing power when setting up CNN. The technology of determination of characteristic features on the face for recognition of emotional manifestations is presented and experimentally investigated. These steps of technology allow to obtain a fairly simple, computationally simple model of representation of emotional states on the face with the help of characteristic vectors grouped by classes of emotions and determine the coefficients of hyperplanes that convexly limit these classes and can be used for subsequent recognition of arbitrary emotional facial expressions.
One of the ways to process an image presented in the form of a set of pixels, in order to further identify, classify the objects present on it is to display the specified set in the form of sets of certain features. Such features are not universal in nature, but rather significantly depend on the tasks under consideration. For certain classes of problems, such features (model) are selected that best allow the application of appropriate methods to solve the problem. The paper considers a class of problems for recognizing the emotional state on a person's face. In, a convolutional neural network (CNN) is used to detect emotions. CNN differs from multilayer perceptron (MLP) in that they have hidden layers called convolutional layers. The proposed method is based on a two-tier CNN system. At the first level, the background of the image is removed to better reflect emotions. A standard CNN network module is used to obtain the primary expression vector (EV). EV is formed by tracking the relevant important points of the face. EV is directly related to changes in facial expression. EV is obtained using a basic perceptron unit plotted on a face image with the background removed. In the proposed model at the last stage there is a nonconvolutionary perceptron layer. Each of the convoluted layers receives input data (images), converts them, and then takes them to the next level. After detecting a face, the CNN filter of the second part captures parts of the face, such as eyes, ears, lips, nose and cheeks. The authors agree that the method has some limitations, and especially requires high computing power when setting up CNN. The technology of determination of characteristic features on the face for recognition of emotional manifestations is presented and experimentally investigated. These steps of technology allow to obtain a fairly simple, computationally simple model of representation of emotional states on the face with the help of characteristic vectors grouped by classes of emotions and determine the coefficients of hyperplanes that convexly limit these classes and can be used for subsequent recognition of arbitrary emotional facial expressions.
Опис
Ключові слова
Facial Expression Recognition Systems
Бібліографічний опис
Інформаційна технологія визначення характерних ознак на обличчі для розпізнавання емоційних проявів / C. О. Іващенко, О. Д. Калита, О. В. Бармак, Т. К. Скрипник // Комп’ютерні системи та інформаційні технології. – 2020. – № 2. – С. 47-53.