prikaz prve stranice dokumenta Biometric refinement of datasets for facial age estimation
  Download
PDF 29.45 MB
doctoral thesis
Biometric refinement of datasets for facial age estimation
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:606173
Bešenić, Krešimir
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Bešenić, K. (2024). Biometric refinement of datasets for facial age estimation (Doctoral thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:606173

Bešenić, Krešimir. "Biometric refinement of datasets for facial age estimation." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:606173

Bešenić, Krešimir. "Biometric refinement of datasets for facial age estimation." Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:606173

Bešenić, K. (2024). 'Biometric refinement of datasets for facial age estimation', Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:606173

Bešenić K. Biometric refinement of datasets for facial age estimation [Doctoral thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 November 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:606173

K. Bešenić, "Biometric refinement of datasets for facial age estimation", Doctoral thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:606173

Metadata
TitleBiometric refinement of datasets for facial age estimation
Title (english)Biometrijsko unapređenje skupova podataka za procjenu dobi iz slika lica
AuthorKrešimir Bešenić
MentorIgor Sunday Pandžić (mentor)
MentorJörgen Ahlberg https://orcid.org/0000-0002-6763-5487 (komentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Universal decimal classification
(UDC)		004 - Computer science and technology. Computing. Data processing
Abstract
This thesis explores the intricate task of facial age estimation with a focus on the role of data in the design of machine-learning-based systems. Supervised machine learning algorithms rely on large amounts of labeled data to learn a mapping between the input data (e.g., facial images or videos) and associated labels (e.g., age or gender). Due to difficulties related to biometric data collection, facial image datasets with biometric trait labels are scarce and frequently limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of heterogeneous but weakly labeled and noisy data. The portion of erroneous samples in such data can be automatically reduced by the proposed unsupervised biometric data filtering method. The problem of data scarcity is further addressed by designing a new public facial image dataset. The proposed derived dataset is thoroughly compared to various state-of-the-art datasets with very favorable results and interesting insights about the significance of data. Video sequences can capture many spatiotemporal dynamic cues related to age progression. However, video-based age estimation is an understudied research field. This may be caused by a lack of well-defined and publicly accessible video benchmark protocols, as well as the absence of video-oriented training data. The former issue was addressed by proposing a carefully designed video age estimation benchmark protocol and making it publicly available. The latter issue was addressed by designing a video-specific age estimation method that leverages pseudo-labeling and semi-supervised learning. The experimental results indicate that using video data unlocks great potential in the age estimation research field.
Abstract (croatian)
Ovaj rad istražuje složen zadatak procjene dobi iz slika lica s naglaskom na ulogu podataka u dizajnu sustava temeljenih na strojnom učenju. Algoritmi nadziranog strojnog učenja oslanjaju se na velike količine označenih podataka kako bi naučili mapiranje između ulaznih podataka (npr. slika ili videozapisa lica) i pripadajućih oznaka (npr. dobi ili spola). Zbog poteškoća vezanih uz prikupljanje biometrijskih podataka, skupovi slika lica s biometrijskim oznakama su rijetki i često ograničeni u pogledu veličine i raznolikosti uzoraka. Metodama za prikupljanje podataka iz internetskih izvora mogu se skupiti velike količine heterogenih, ali nepouzdano označenih podataka. Udio pogrešnih uzoraka u takvim skupovima podataka može se automatski smanjiti predloženom metodom za nenadzirano biometrijsko filtriranje podataka. Problem nedostatka podataka za učenje dodatno je adresiran dizajniranjem nove javno dostupne baze slika lica. Predloženi izvedeni skup podataka temeljito je uspoređen s trenutno najboljim javno dostupnim skupovima podataka s vrlo povoljnim rezultatima i zanimljivim uvidima o značaju podataka. Videozapisi mogu sadržavati mnoge prostorno-vremenske dinamičke značajke relevantne za procjenu dobi, no procjena dobi iz video podataka je slabo istraženo znanstveno polje. Potencijalni uzrok za to je nedostatak dobro definiranih i javno dostupnih evaluacijskih protokola, kao i nedostatak označenih video podataka za učenje. Prvi od navedenih problema riješen je dizajniranjem javno dostupnog protokola za procjenu dobi iz video podataka. Drugom problemu pristupilo se dizajniranjem metode za procjenu dobi iz video podataka koja koristi pseudo-označavanje i djelomično nadzirano učenje. Eksperimentalni rezultati pokazuju da korištenje video podataka otkriva veliki potencijal u području istraživanja procjene dobi.
Keywords
Keywords (croatian)
Languageenglish
URN:NBNurn:nbn:hr:168:606173
Promotion2024
Study programmeTitle: Computer Science
Study programme type: university
Study level: postgraduate
Academic / professional title: Doktor znanosti (Doktor znanosti)
Type of resourceText
Extent135 str.
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-09-03 12:44:49