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master's thesis
Algoritmi za prepoznavanje lica
2016. urn:nbn:hr:126:340618
Nikolić, Kristina
Josip Juraj Strossmayer University of Osijek
Department of Mathematics
Chair of Applied Mathematics
Applied Mathematics Research Group

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Nikolić, K. (2016). Algoritmi za prepoznavanje lica (Master's thesis). Retrieved from https://urn.nsk.hr/urn:nbn:hr:126:340618

Nikolić, Kristina. "Algoritmi za prepoznavanje lica." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2016. https://urn.nsk.hr/urn:nbn:hr:126:340618

Nikolić, Kristina. "Algoritmi za prepoznavanje lica." Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, 2016. https://urn.nsk.hr/urn:nbn:hr:126:340618

Nikolić, K. (2016). 'Algoritmi za prepoznavanje lica', Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, accessed 21 September 2022, https://urn.nsk.hr/urn:nbn:hr:126:340618

Nikolić K. Algoritmi za prepoznavanje lica [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Department of Mathematics; 2016 [cited 2022 September 21] Available at: https://urn.nsk.hr/urn:nbn:hr:126:340618

K. Nikolić, "Algoritmi za prepoznavanje lica", Master's thesis, Josip Juraj Strossmayer University of Osijek, Department of Mathematics, Osijek, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:126:340618

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Metadata
TitleAlgoritmi za prepoznavanje lica
Title (english)Face Recognition Algorithms
AuthorKristina Nikolić
MentorDarija Marković (mentor)
MentorSlobodan Jelić (sumentor)
Committee memberKristian Sabo (predsjednik povjerenstva)
Committee memberDarija Marković (član povjerenstva)
Committee memberZoran Tomljanović (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Department of Mathematics
(Chair of Applied Mathematics)
(Applied Mathematics Research Group)
Osijek
Defense date and country2016-10-21, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U diplomskom radu opisana je primjena algoritama za prepoznavanje lica u samom po- četku razvoja algoriama i danas. Opisani su glavni pristupi koji se koriste prilikom osmišljavanja algoritama kao što su psihloški i matematički pristup. Navedene su najbitnije sastavnice oba pristupa te je dan uvid u općeniti način pristupanja prepoznavanju lica. Algoritmi za prepoznavanje lica su u radu podijeljeni na dvije glavne kategorije - algoritmi koji koriste dvodimenzionalni i algoritmi koji koriste trodimenzionalni prikaz lica za prepoznavanje. Za svaku od kategorija navedena su po tri algoritma. Od dvodimenzionalnih algoritama opisane su Nixonova metoda, metoda Principal Component Analysis i Linear Discriminant Analysis. Metode algoritama koji koriste trodimenzionalni prikaz koje su opisane u radu su trodimenzionalno prepoznavanje bez rekonstrukcije lica, 3D Morphable Model i metoda bazirana na komponentama. Također, opisan je praktični dio rada u kojem je dana implemenatacija metode Principal Component Analysis i opisana je baza fotografija korištena u navedenoj implementaciji. U dijelovima isstraživanja ovoga rada korištena je FERET baza fotografija lica prikupljena u sklopu FERET programa, sponzorirana od strane ureda DOD Counterdrug Technology Development Program. Osim toga, pojašnjen je način korištenja implementiranog algoritma
Abstract (english)
This thesis describes usage of face recognition algorithms since they first appeared until today. There is given insight into main approaches used in developing algorithms as psychological and mathematical approach. The most important components of both approaches are specified and there are explained general methods of face recognition. Face recognition algorithms are separated into two categories - algorithms using two-dimensional and three-dimensional face representations. There are described three methods for each category of algorithms. Among two-dimensional algorithms there are described Nixon method, Principal Component Analysis method and Linear Discriminant Method. Three-dimensional algorithms category consists of explanation od method for face recognition without face reconstruction, 3D Morphable Model method and method based on face components. Further more, there is explanation of practical part of this thesis where implementation of Principal Component Analysis method is given and there is description of images database used in implementation. Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office. Also, there is explained usage of implemented algorithm.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:126:340618
Study programmeTitle: Mathematics; specializations in: Financial and Statistical Mathematics, Mathematics and Computer Science, Industrial and Applied Mathematics
Course: Mathematics and Computer Science
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2016-11-02 07:18:06