Authors:
Krešimir Bešenić
1
;
Jörgen Ahlberg
2
and
Igor S. Pandžić
1
Affiliations:
1
Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb and Croatia
;
2
Computer Vision Laboratory, Linköping University, 58183 Linköping and Sweden
Keyword(s):
Filtering, Unsupervised, Biometric, Web-Scraping, Age, Gender.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
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.