Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation (CROSBI ID 693772)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bešenić, Krešimir ; Ahlberg, Jörgen ; Pandžić, Igor
engleski
Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation
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.
Filtering, Unsupervised, Biometric, Web-Scraping, Age, Gender
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Podaci o prilogu
209-217.
2019.
objavljeno
10.5220/0007257202090217
Podaci o matičnoj publikaciji
SCITEPRESS
978-989-758-354-4
Podaci o skupu
International Conference on Computer Vision Theory and Applications (VISAPP)
predavanje
25.02.2019-27.02.2019
Prag, Češka Republika
Povezanost rada
Informacijske i komunikacijske znanosti, Interdisciplinarne tehničke znanosti, Računarstvo