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Picking out the bad apples: unsupervised biometric data filtering for refined age estimation

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Abstract

Introduction 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, facial image datasets with biometric trait labels are scarce and usually limited in terms of size and sample diversity. Web-scraping approaches for automatic data collection can produce large amounts of weakly labeled and noisy data. This work is focused on picking out the bad apples from web-scraped facial datasets by automatically removing erroneous samples that impair their usability. The unsupervised facial biometric data filtering method presented in this work greatly reduces label noise levels in web-scraped facial biometric data. Experiments on two large state-of-the-art web-scraped datasets demonstrate the effectiveness of the proposed method with respect to real and apparent age estimation based on five different age estimation methods. Furthermore, we apply the proposed method, together with a newly devised strategy for merging multiple datasets, to data collected from three major web-based data sources (i.e., IMDb, Wikipedia, Google) and derive the new Biometrically Filtered Famous Figure Dataset or B3FD. The proposed dataset, which is made publicly available, enables considerable performance gains for all tested age estimation methods and age estimation tasks. This work highlights the importance of training data quality compared to data quantity and selection of the estimation method.

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Data availability

The proposed dataset is available at https://github.com/kbesenic/B3FD.

Notes

  1. https://images.google.com/.

  2. www.flickr.com.

  3. www.imdb.com.

  4. https://en.wikipedia.org/.

  5. http://dlib.net.

  6. https://pjreddie.com/darknet/tiny-darknet/.

  7. https://github.com/kbesenic/B3FD.

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Funding

The author K. Bešenić receives Ph.D. scholarship from the company Visage Technologies.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Krešimir Bešenić & Igor S. Pandžić

  2. Computer Vision Laboratory, Linköping University, 58183, Linköping, Sweden

    Jörgen Ahlberg

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  1. Krešimir Bešenić
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  3. Igor S. Pandžić
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Correspondence to Krešimir Bešenić.

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The author K. Bešenić is employed by Visage Technologies. Authors J. Ahlberg and I. S. Pandžić are members of Visage Technologies’ board of directors. All three authors own stock in the company.

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Bešenić, K., Ahlberg, J. & Pandžić, I.S. Picking out the bad apples: unsupervised biometric data filtering for refined age estimation. Vis Comput 39, 219–237 (2023). https://doi.org/10.1007/s00371-021-02323-y

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