Naslov
Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation

Autori
Bešenić, Krešimir ; Ahlberg, Jörgen ; Pandžić, Igor

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP / - : SCITEPRESS, 2019, 209-217

ISBN
978-989-758-354-4

Skup
International Conference on Computer Vision Theory and Applications (VISAPP)

Mjesto i datum
Prag, Češka Republika, 25.02.2019. - 27.02.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Filtering, Unsupervised, Biometric, Web-Scraping, Age, Gender

Sažetak
Availability of large training datasets was essential for the recent advancement and success of deep learning methods. Due to the difficulties related to biometric data collection, datasets with age and gender annotations are scarce and usually limited in terms of size and sample diversity. Web- scraping approaches for automatic data collection can produce large amounts weakly labeled noisy data. The unsupervised facial biometric data filtering method presented in this paper greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web- scraped facial datasets demonstrate the effectiveness of the proposed method, with respect to training and validation scores, training convergence, and generalization capabilities of trained age and gender estimators.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

POVEZANOST RADA