loading
Documents

Research.Publish.Connect.

Paper

Authors: Krešimir Bešenić 1 ; Jörgen Ahlberg 2 and Igor Pandžić 1

Affiliations: 1 Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia ; 2 Computer Vision Laboratory, Linköping University, 58183 Linköping, Sweden

ISBN: 978-989-758-354-4

Keyword(s): Filtering, Unsupervised, Biometric, Web-Scraping, Age, Gender.

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.237.76.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bešenić, K.; Ahlberg, J. and Pandžić, I. (2019). Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 209-217. DOI: 10.5220/0007257202090217

@conference{visapp19,
author={Krešimir Bešenić. and Jörgen Ahlberg. and Igor S. Pandžić.},
title={Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={209-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007257202090217},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Unsupervised Facial Biometric Data Filtering for Age and Gender Estimation
SN - 978-989-758-354-4
AU - Bešenić, K.
AU - Ahlberg, J.
AU - Pandžić, I.
PY - 2019
SP - 209
EP - 217
DO - 10.5220/0007257202090217

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.