Deep Verification Learning

Fieke Hillerström, Raymond Veldhuis, Luuk Spreeuwers

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Deep learning for biometrics has increasingly gained attention over the last years.
The expansion of computational power and the increasing dataset sizes, increased
verification performances. However, large datasets are not available for every ap-
plication. We introduce Deep Verification Learning, to reduce network complex-
ity and train on smaller datasets. Deep Verification Learning takes two images
to be verified at the input of a network, and trains directly towards a verification
score. We applied Deep Verification Learning on the face verification task, also
it could be extended to other biometric modalities.
Original languageEnglish
Title of host publicationProceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux
Subtitle of host publicationMay 11-12, 2017, Delft University of Technology, Delft, the Netherlands
EditorsRichard Heusden, Jos H. Weber
Place of PublicationDelft
PublisherDelft University of Technology
Pages97-104
ISBN (Print)978-94-6186-811-4
Publication statusPublished - 2017
Event38th WIC Symposium on Information Theory in the Benelux 2017 - Delft, Netherlands
Duration: 11 May 201712 May 2017
Conference number: 38

Conference

Conference38th WIC Symposium on Information Theory in the Benelux 2017
CountryNetherlands
CityDelft
Period11/05/1712/05/17

Fingerprint

Biometrics
Complex networks
Deep learning

Keywords

  • SCS-Safety
  • FISWG
  • Eyebrows
  • Human verification performance

Cite this

Hillerström, F., Veldhuis, R., & Spreeuwers, L. (2017). Deep Verification Learning. In R. Heusden, & J. H. Weber (Eds.), Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands (pp. 97-104). Delft: Delft University of Technology.
Hillerström, Fieke ; Veldhuis, Raymond ; Spreeuwers, Luuk. / Deep Verification Learning. Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands. editor / Richard Heusden ; Jos H. Weber. Delft : Delft University of Technology, 2017. pp. 97-104
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Hillerström, F, Veldhuis, R & Spreeuwers, L 2017, Deep Verification Learning. in R Heusden & JH Weber (eds), Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands. Delft University of Technology, Delft, pp. 97-104, 38th WIC Symposium on Information Theory in the Benelux 2017, Delft, Netherlands, 11/05/17.

Deep Verification Learning. / Hillerström, Fieke; Veldhuis, Raymond; Spreeuwers, Luuk.

Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands. ed. / Richard Heusden; Jos H. Weber. Delft : Delft University of Technology, 2017. p. 97-104.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - Deep learning for biometrics has increasingly gained attention over the last years.The expansion of computational power and the increasing dataset sizes, increasedverification performances. However, large datasets are not available for every ap-plication. We introduce Deep Verification Learning, to reduce network complex-ity and train on smaller datasets. Deep Verification Learning takes two imagesto be verified at the input of a network, and trains directly towards a verificationscore. We applied Deep Verification Learning on the face verification task, alsoit could be extended to other biometric modalities.

AB - Deep learning for biometrics has increasingly gained attention over the last years.The expansion of computational power and the increasing dataset sizes, increasedverification performances. However, large datasets are not available for every ap-plication. We introduce Deep Verification Learning, to reduce network complex-ity and train on smaller datasets. Deep Verification Learning takes two imagesto be verified at the input of a network, and trains directly towards a verificationscore. We applied Deep Verification Learning on the face verification task, alsoit could be extended to other biometric modalities.

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KW - Eyebrows

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Hillerström F, Veldhuis R, Spreeuwers L. Deep Verification Learning. In Heusden R, Weber JH, editors, Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands. Delft: Delft University of Technology. 2017. p. 97-104