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undergraduate thesis
Primjena dubokog učenja za detekciju lica
2022. urn:nbn:hr:168:888053
Vučinić, Anton
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Vučinić, A. (2022). Primjena dubokog učenja za detekciju lica (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:888053

Vučinić, Anton. "Primjena dubokog učenja za detekciju lica." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:888053

Vučinić, Anton. "Primjena dubokog učenja za detekciju lica." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:888053

Vučinić, A. (2022). 'Primjena dubokog učenja za detekciju lica', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 13 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:888053

Vučinić A. Primjena dubokog učenja za detekciju lica [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 November 13] Available at: https://urn.nsk.hr/urn:nbn:hr:168:888053

A. Vučinić, "Primjena dubokog učenja za detekciju lica", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:888053

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Metadata
TitlePrimjena dubokog učenja za detekciju lica
Title (english)Deep Learning for Face Detection
AuthorAnton Vučinić
MentorKrešimir Križanović (mentor)
Committee memberKrešimir Križanović (predsjednik povjerenstva)
Committee memberFilip Bosnić (član povjerenstva)
Committee memberJosipa Lipovac (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-08-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad bavi se proučavanjem metoda detekcija lica na fotografijama. Predstavlja se podatkovni skup koji će se koristiti kroz cijeli rad te se prvo na njega primjenjuje model MTCNN. Nakon toga se dobiveni rezultati uspoređuju sa starijom metodom detekcije objekata zvanom kaskadna klasifikacija. Te se za kraj rada pokušava trenirati vlastiti model temeljen na dubokim konvolucijskim mrežama. Komentiraju se prednosti i nedostatci svih pristupa ovom problemu.
Abstract (english)
This paper deals with the problem of detecting faces on images. It compares different methods to do so and comments on each of their advantages and disadvantages. First the dataset that will be used throughout the paper is introduced. Then using the MTCNN model predictions are made on the subset of the dataset and performance is measured. After that the dataset is once again marked, this time using OpenCV’s cascade classifier with pretrained Haar-features. Finally at the end of the paper we developed our own deep convolutional neural network and the results of all three methods are compared.
Languagecroatian
URN:NBNurn:nbn:hr:168:888053
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:25:51