### Abstract

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 language | English |
---|---|

Title of host publication | Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux |

Subtitle of host publication | May 11-12, 2017, Delft University of Technology, Delft, the Netherlands |

Editors | Richard Heusden, Jos H. Weber |

Place of Publication | Delft |

Publisher | Delft University of Technology |

Pages | 97-104 |

ISBN (Print) | 978-94-6186-811-4 |

Publication status | Published - 2017 |

Event | 38th WIC Symposium on Information Theory in the Benelux 2017 - Delft, Netherlands Duration: 11 May 2017 → 12 May 2017 Conference number: 38 |

### Conference

Conference | 38th WIC Symposium on Information Theory in the Benelux 2017 |
---|---|

Country | Netherlands |

City | Delft |

Period | 11/05/17 → 12/05/17 |

### Fingerprint

### Keywords

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

### Cite this

*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.

}

*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.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Deep Verification Learning

AU - Hillerström, Fieke

AU - Veldhuis, Raymond

AU - Spreeuwers, Luuk

PY - 2017

Y1 - 2017

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.

KW - SCS-Safety

KW - FISWG

KW - Eyebrows

KW - Human verification performance

M3 - Conference contribution

SN - 978-94-6186-811-4

SP - 97

EP - 104

BT - Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux

A2 - Heusden, Richard

A2 - Weber, Jos H.

PB - Delft University of Technology

CY - Delft

ER -