The Method of Using Production Rules in Neural Network Recognition of Emotions by Facial Geometry

Procedure for Processing Biometric Parameters Based on Wavelet Transformations

“…Based on the proposed approach and results [1,4,20], a procedure for filtering biometric parameters associated with two-dimensional color or monochrome images has been developed. In the basic case, the implementation of the procedure is to perform the following steps:…”

“…In modern conditions, the development of covert monitoring tools (CMT) of the person and emotions of operators of information management systems (IMS) based on biometric parameters is one of those tasks, the solution of which will significantly increase the effectiveness of detection of unauthorized access to information and the effectiveness of adequacy assessment. psycho-emotional state of staff regarding the possibility of solving their functional responsibilities [1][2][3]. These tools are successfully used in office information systems for protection against insiders, in distance learning systems for automatic control of the mode of submission of educational materials and to verify the authenticity of the student's identity when passing exams.…”

“…In most cases, the theoretical basis of the CMT of personality and emotions is a neural network analysis of such biometric parameters as facial images and iris images, ie biometric parameters that correlate with two-dimensional images and are characterized by geometric indicators [1,3,4]. This is due to the availability of registration tools, high affinity of these biometric parameters with the identity and emotions of person , proven neural network solutions and the availability of available representative labeled databases of facial images and iris images, which greatly simplifies the process of developing neural network models.…”

“…This is due to the availability of registration tools, high affinity of these biometric parameters with the identity and emotions of person , proven neural network solutions and the availability of available representative labeled databases of facial images and iris images, which greatly simplifies the process of developing neural network models. However, in practice, the effectiveness of CMT largely depends on the quality of registration of the image of the face and the image of the iris, which is explained by the presence of typical obstacles that can not be eliminated by improving the characteristics of hardware [1,2,5,6].…”

Метод Нейромережевого Розпізнавання Емоцій По Зображенню Обличчя.

“…У той же час важливим напрямом підвищення ефективності ЗАРЕ є "інтелектуалізація" методів розпізнавання за допомогою використання нейромережевих рішень [1,7]. Крім літературних джерел [8][9][10][11][17][18][19][20][21][22], перспективність даного напрямку підтверджується успішним застосуванням нейромережевих ЗАРЕ в проектах «Oxford» (azure.microsoft.com/ruru/services/cognitive-services), «Noldus» (www.noldus.com/human-behavior-research/products/ facereader) та «Eyeris» (www.eyeris.ai). Разом з тим велика кількість хибних спрацювань, довготривалий термін та нестабільність навчання, недостатня адаптація до багатьох особливостей сучасного стану ІУС, що пов'язана в першу з методологічними недоліками, значно обмежують їх практичну цінність.…”