Kombiniranje modelov razvrščanja vzorcev za razpoznavanje čustvenih izrazov iz posnetkov obraza v nekontroliranem okolju : diplomsko delo

Osojnik, Juš

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Title:	Kombiniranje modelov razvrščanja vzorcev za razpoznavanje čustvenih izrazov iz posnetkov obraza v nekontroliranem okolju : diplomsko delo
Authors:	ID Osojnik, Juš (Author) ID Potočnik, Božidar (Mentor) More about this mentor... ID Šavc, Martin (Comentor)
Files:	UN_Osojnik_Jus_2023.pdf (4,01 MB) MD5: 2A58C9DEB8C1F36D8FE37E33E0495ADD
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V tem delu smo se ukvarjali z razpoznavanjem čustvenih izrazov v nekontroliranem okolju. Uporabljali smo metodo prenosnega učenja, kjer smo učili arhitekture konvolucijskih nevronskih mrež: EfficientNetB0, ResNet50, DenseNet121, InceptionV3 in Xception, na naboru podatkovnih zbirk FER-2013, AffectNet, AFEW/SFEW in Aff-Wild2. Modele smo nato kombinirali na osnovi rezultatov z metodama povprečenja in glasovanja. Modele smo kombinirali tudi na osnovi izluščenih značilnic. Uspešnost modelov smo merili po metrikah natančnosti in ocene F1. Na podatkovni zbirki FER-2013 smo dosegli najboljšo natančnost 72 %, na zbirkah AffectNet 67 %, AFEW/SFEW 47 % in Aff-Wild2 52 % natančnost. Z našimi rezultati smo se približali najuspešnejšim raziskavam, ki so na posameznih podatkovnih zbirkah dosegle natančnosti: FER-2013 77 %, AffectNet 67 %, AFEW/SFEW 54 % in Aff-Wild2 52 %.
Keywords:	prepoznavanje čustvenih izrazov, slike obrazov, globoke nevronske mreže, modelno združevanje, ekstrakcija značilnic, okolje Keras
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[J. Osojnik]
Year of publishing:	2023
Number of pages:	1 spletni vir (1 datoteka PDF (XV, 78 f.))
PID:	20.500.12556/DKUM-85832
UDC:	004.93(043.2)
COBISS.SI-ID:	174792451
Publication date in DKUM:	13.10.2023
Views:	647
Downloads:	106
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:	A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:	13.09.2023

Secondary language

Language:	English
Title:	Combining classification models for facial emotion recognition in uncontrolled environments
Abstract:	In this work, we focused on facial emotion recognition in an uncontrolled environment. We used the transfer learning method, where we trained the architectures of convolutional neural networks: EfficientNetB0, ResNet50, DenseNet121, InceptionV3, Xception, on the datasets FER-2013, AffectNet, AFEW/SFEW, and Aff-Wild2. We then combined the models based on the results using both averaging and voting methods. We also combined the models based on extracted features. The performance of the models was measured using accuracy and F1 score metrics. On the FER-2013 dataset, we achieved an accuracy of 72 %, on AffectNet 67 %, on AFEW/SFEW 47 %, and on Aff-Wild2 52 %. In our research we aproached the state of the art results achieved on the individual datasets, which are: FER-2013 77 %, AffectNet 67 %, AFEW/SFEW 54 % and Aff-Wild2 52 %.
Keywords:	Facial emotion recognition, Facial images, Deep neural networks, Model, ensemble, Feature extraction, Keras applications

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