StatusThe thesis was presented on the 9 November, 2016
Approved by NCAA on the 28 December, 2016
Abstract– 0.86 Mb / in english
– 1.01 Mb / in romanian
ThesisCZU 519. 95
3.98 Mb /
The thesis was elaborated at the Institute of Mathematics and Computer Science of the Academy of Sciences of Moldova, Chisinau, in 2016. The thesis is written in English and contains Introduction, 3 chapters, general conclusions and recommendations, bibliography of 109 titles. The main text amounts to 121 pages. This work includes: 37 figures, 2 tables, 44 formulas, and 5 annexes. The results are published in 8 scientific papers.
The area of the present studies is the field of emotion and action recognition using modular neural networks.
The aim and objectives of this research is to develop a tool for classification of human reactions (including facial features and body movements) into typical and non-typical in a certain environment. This tool provides statistical observations and measurements of human emotional states during an interaction session with a software product (or, optionally, with a hardware plus software complex).
Scientific novelty is a novel modular neural network architecture, constituted from two separate parts and combine the results to introduce the classification of the infrared sensor inputs, which is the first system of this kind, being applied both to emotion and human action recognition.
The important solved scientific problem is elaboration of a multimodal method for classification of human reactions (joining emotions and actions) into typical and non-typical in a certain environment, that ensures an effective functioning of systems destined to human actions monitoring in real time.
Theoretical significance. Our research solutions provide ground for solving of following problems: formulation of the tool’s architecture for robust classification of emotions and gestures of a human subject into typical vs. non-typical; the substantiation of the possibility and efficiency of using deep learning in an integrated approach for the detection of expression of the whole body in real time.
Practical value: this kind of classification task is very useful in different applications, where the number of gestures of the human is limited, such as: customers at the various types of automated machines, drivers, assembly line workers, hospital patients etc.