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master's thesis
Primjena algoritama strojnog učenja u prepoznavanju lica
2020. urn:nbn:hr:200:060378
Puljko, Benjamin
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Computer Engineering and Automation
Chair of Automation and Robotics

Cite this document

Puljko, B. (2020). Primjena algoritama strojnog učenja u prepoznavanju lica (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:060378

Puljko, Benjamin. "Primjena algoritama strojnog učenja u prepoznavanju lica." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:060378

Puljko, Benjamin. "Primjena algoritama strojnog učenja u prepoznavanju lica." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2020. https://urn.nsk.hr/urn:nbn:hr:200:060378

Puljko, B. (2020). 'Primjena algoritama strojnog učenja u prepoznavanju lica', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 22 January 2021, https://urn.nsk.hr/urn:nbn:hr:200:060378

Puljko B. Primjena algoritama strojnog učenja u prepoznavanju lica [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2020 [cited 2021 January 22] Available at: https://urn.nsk.hr/urn:nbn:hr:200:060378

B. Puljko, "Primjena algoritama strojnog učenja u prepoznavanju lica", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:200:060378

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Metadata
TitlePrimjena algoritama strojnog učenja u prepoznavanju lica
Title (english)Application of machine learning algorithms in facial recognition
AuthorBenjamin Puljko
MentorEmmanuel Karlo Nyarko (mentor)
Committee memberEmmanuel Karlo Nyarko (predsjednik povjerenstva)
Committee memberRatko Grbić (član povjerenstva)
Committee memberDamir Filko (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Computer Engineering and Automation)
(Chair of Automation and Robotics)
Osijek
Defense date and country2020-07-24, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U ovom radu ispitana su četiri algoritma strojnog učenja koja su se upotrebljavala za klasifikaciju. Prvotno je pohranjen skup podataka koji je zatim obrađen na način da su detektirana lica, izrezana, poravnata te pohranjena u podatak koji se kasnije učitava radi lakšeg izvršavanja eksperimenata. U prvom eksperimentu se za izdvajanje značajki i smanjenje dimenzionalnosti upotrebljavala analiza glavnih komponenata (PCA), dok je za drugi eksperiment upotrebljen facenet model za izdvajanje značajki. Zaključeno je da je facenet superiorniji u izdvajanju značajki od PCA. U trećem eksperimentu je također rabljen facenet model, ali s povećanim brojem klasa u odnosu na prvi i drugi eksperiment. U posljednjem, četvrtom eksperimentu, uspoređena je vgg16 naučena neuronska mreža s facenet naučenim modelom. Vgg16 model imao je točnost od 94,6%, dok je facenet model imao točnost od nevjerojatnih 99,8%. Zaključeno je da su neuronske mreže daleko učinkovitije u izdvajanju značajki te sam klasifikacijski algoritam ne igra veliku ulogu u točnoj klasifikaciji, ukoliko su značajke dobro izdvojene.
Abstract (english)
In this paper, four machine learning algorithms have been tested for classification. Initially, the dataset was processed in such a way that the detected faces were cut, aligned and stored in a file for later use. In the first experiment, principal component analysis was used for feature extraction and dimensionality reduction while for the second experiment, the pretrained facenet model was used. From the experiments, it was concluded that facenet is superior to PCA in feature extraction. In the third experiment, facenet model was used, but with a greater number of classes than in the first two experiments. In the last, fourth experiment, the vgg16 pretrained neural network was compared to the facenet pretrained model. Vgg16 model had an accuracy of 94,6% while the facenet model had an amazing accuracy of 99,8%. In conclusion, neural networks are far more efficient in feature extraction and the classification algorithm itself doesn't play a big role in correctly classifying the face, if the features are well extracted.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:060378
Study programmeTitle: UNIVERSITY STUDY AUTOMOBILE COMPUTER AND COMMUNICATION
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka automobilskog računarstva i komunikacija (magistar/magistra inženjer/inženjerka automobilskog računarstva i komunikacija)
Type of resourceText
File originBorn digital
Access conditionsOpen access
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Created on2020-09-15 15:15:52