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master's thesis
Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:703511
Marijić, Marko
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Marijić, M. (2022). Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:703511

Marijić, Marko. "Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:703511

Marijić, Marko. "Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:703511

Marijić, M. (2022). 'Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:703511

Marijić M. Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2022 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:235:703511

M. Marijić, "Usporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:703511

Metadata
TitleUsporedba i evaluacija baza podataka emocija koristeći konvolucijsku neuronsku mrežu
Title (english)Comparison and evaluation of emotion databases using a convolutional neural network
AuthorMarko Marijić
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
Unutar ovog rada uspoređuju se dostupne baze podataka za automatsko prepoznavanje emocija s lica osobe. Zadatak je upotrebom VGG16 konvolucijske neuronske mreže provesti trening s tri različite baze podataka te evaluirati njihov rad. Koristeći isti model mreže, mogu se donijeti konkretni zaključci o kvaliteti podataka unutar svake baze. Uvodni dio rada pojašnjava što je to duboko učenje te predstavlja dosadašnja postignuća u području automatskog prepoznavanja emocija. Počevši od modela umjetnog neurona, objasniti će se cjelokupna arhitektura mreže. Definirati će se slojevi konvolucijske neuronske mreže, a zatim i objasniti svaki od njih. Nadalje, predstaviti će se baze podataka te opisati proces treninga. Na kraju će se prikazati dobiveni rezultati te će se komparativnom analizom donijeti zaključak o kvaliteti rada svake mreže. Za potrebe istraživanja u ovom radu korištene su brojne znanstvene metode od kojih se izdvajaju znanstvene metode analize i sinteze te metode indukcije i dedukcije.
Abstract (english)
Within this paper the available databases for automatic emotion recognition from facial features will be compared. The task is by using same VGG16 convolution neural network conduct training with three different databases and evaluate their work. Using the same network model, specific conclusions can be made about the quality of the date within each database. The introductory part of the paper explains what deep learning is and presents the achievements so far in the field of automatic emotion recognition. Starting with an artificial neuron model, the overall architecture of the network will be explained. The layers of convolutional neural networks will be defined and then each of them will be explained. Furthermore, databases will be presented, and the training process will be described. At the end, the obtained results will be presented, and a conclusion will be drawn about the quality of work of each network through a comparative analysis. For the purposes of research in this work, numerous scientific methods are used, of which the scientific methods of analysis and synthesis and the methods of induction and deduction stand out.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:703511
Study programmeTitle: Mechanical Engineering; specializations in: Design, Process and Energy Engineering, Production Engineering, Engineering Modelling and Computer Simulation, Marine Engineering, Industrial Engineering and Management, Materials Engineering, Mechatronics and Robotics, Autonomous Systems
Course: Autonomous Systems
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-11-30 20:29:58