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master's thesis
Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:060838
Bračun, Juraj
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

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

Cite this document

Bračun, J. (2022). Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:060838

Bračun, Juraj. "Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:060838

Bračun, Juraj. "Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:060838

Bračun, J. (2022). 'Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:060838

Bračun J. Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje [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:235:060838

J. Bračun, "Identifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:060838

Metadata
TitleIdentifikacija osobe temeljena na TensorFlow biblioteci za strojno učenje
Title (english)Person identification based on the TensorFlow machine learning library
AuthorJuraj Bračun
MentorTomislav Stipančić (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-07-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
Područje umjetne inteligencije u današnje se vrijeme koristi gotovo u svakoj grani tehnologije kao i u sve većem broju strojeva i predmeta kojima svaki dan rukujemo. Neki su se dijelovi umjetne inteligenciju razvili do stupnja gdje je čovjek postao gotovo ovisan o rezultatima koje takvi, pametni sustavi daju. Primjena takvih sustava uvelike je promijenila način na koji ljudi pristupaju određenim tehničkim problemima. Njihova je glavna prednost, mogućnost samostalnog i kontinuiranog učenja koje konstantno unaprjeđuje preciznost i točnost takvih sustava, čime se oponaša djelovanje ljudskog mozga koji na isti način funkcionira u realnom okruženju. Jedna vrsta takvih pametnih sustava koja je postala veoma popularna u zadnjih desetak ili više godina, svakako je sposobnost računala da prepozna različita ljudska lica. Iako je taj proces u ljudskom mozgu gotovo automatiziran te ne predstavlja nikakav poseban napor, za računalo to je ozbiljan izazov kojega nije bilo lako riješiti. U ovome će se radu kreirati upravo jedan program čiji će zadatak biti prepoznavanje ljudskih lica u realnom vremenu. Da bi se to moglo ostvariti, potrebno je koristiti dva odvojena sustava, jedan zadužen za detekciju lica na slici, a drugi zadužen za prepoznavanje osobe čije se lice nalazi na slici. Korištenje oba sustava temelji se na tzv. prenesenom učenju, gdje se već kreirani sustavi prilagođavaju i modificiraju za neke specifične potrebe, u ovom slučaju za izradu sustava za prepoznavanje lica.
Abstract (english)
Today, artificial intelligence is being used in almost every aspect of technology, as well as in growing number of machines and gadgets that we use on daily basis. Some areas of artificial intelligence are so advanced, that people almost depend on the results which such smart systems give on their output. Application of those systems has greatly changed the way we approach different technical problems. Main advantage of modern, smart systems is the capability to continuously learn by themselves, which results in much higher accuracy and precision. This learning is based on imitating human brain that works the same way in real environment. One example of these smart systems is definitely computer ability to recognize human faces, which has become very popular in the last 10 or so years. Although, the process of recognizing faces is almost automated in human brain, it has been a real challenge to implement such thing in computers and machines. In this paper, one such facial recognition system will be created. The main task of that system will be recognizing faces in real time using web camera. To accomplish that, it is necessary to use two separate subsystems, one for the face detection and the other for face recognition once face has been detected. Both subsystems are based on transfer learning, which means that they are already created and only need to be slightly modified for this special case of recognizing faces in real time.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:060838
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 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) icon
Created on2022-07-12 13:53:46