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undergraduate thesis
Određivanje starosti osobe sa slike
2016. urn:nbn:hr:200:913527
Damjanović, Tamara
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

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Damjanović, T. (2016). Određivanje starosti osobe sa slike (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:913527

Damjanović, Tamara. "Određivanje starosti osobe sa slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2016. https://urn.nsk.hr/urn:nbn:hr:200:913527

Damjanović, Tamara. "Određivanje starosti osobe sa slike." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2016. https://urn.nsk.hr/urn:nbn:hr:200:913527

Damjanović, T. (2016). 'Određivanje starosti osobe sa slike', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 28 January 2025, https://urn.nsk.hr/urn:nbn:hr:200:913527

Damjanović T. Određivanje starosti osobe sa slike [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2016 [cited 2025 January 28] Available at: https://urn.nsk.hr/urn:nbn:hr:200:913527

T. Damjanović, "Određivanje starosti osobe sa slike", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:200:913527

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Metadata
TitleOdređivanje starosti osobe sa slike
Title (english)Persons age estimation form the image
AuthorTamara Damjanović
MentorIrena Galić (mentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberHrvoje Glavaš (član povjerenstva)
Committee memberJosip Job (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2016-09-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Data Processing
Abstract
Karakteristike ljudskog lica oduvijek su predmet proučavanja društvenih znanosti. Razvojem tehnologije i industrije te zbog sve veće interakcije čovjeka i računala nastoje se izlučiti značajke ljudskog lica koje bi računalu omogućile razlikovanje identiteta, spola, raspoloženja i starosti pojedinaca. U ovom radu naglasak je upravo na razlikovanju starosne dobi pojedinaca. Primjenom određenih matematičkih postupaka (analiza glavnih komponenti, linearna diskriminantna analiza) smanjena je dimenzionalnost podataka, tj. izlučene su potencijalno bitne značajke za određivanje starosne dobi pojedinca. Algoritmi strojnog učenja primjenjuju se u postupku klasifikacije. Opisana su dva često korištena - algoritam k najbližih susjeda te stroj s potpornim vektorima. S obzirom da se računalni vid ubrzano razvija, poboljšanje već postojećih rješenje može se očekivati, kao i nova rješenja.
Abstract (english)
Characteristics of human face have always been studied in social sciences. Development of technology and industry, as well as increased interaction between human and machine, have increased the need for computer based estimation of identity, gender, mood and age of person. In this paper emphasis has been put on age estimation. Application of mathematical methods, such as principal component analysis (PCA) and linear discriminant analysis (LDA), has enabled dimensionality reduction, i.e. extraction of potentially significant features for determining age of individual. Machine learning algorithms are used for classification. Here have been described two frequently used algorithms – k-nearest neighbors (k-NN) and support vector machine (SVM). Due to rapidly developing of computer vision, improved solutions, as well as new ones, can be expected
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:913527
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
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
Access conditionsOpen access
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Created on2020-01-13 11:15:57