Thermal to visible face recognition using deep autoencoders

Alperen Kantarci, Hazim Kemal Ekenel

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

18 Citations (Scopus)

Abstract

Visible face recognition systems achieve nearly perfect recognition accuracies using deep learning. However, in lack of light, these systems perform poorly. A way to deal with this problem is thermal to visible cross-domain face matching. This is a desired technology because of its usefulness in night time surveillance. Nevertheless, due to differences between two domains, it is a very challenging face recognition problem. In this paper, we present a deep autoencoder based system to learn the mapping between visible and thermal face images. Also, we assess the impact of alignment in thermal to visible face recognition. For this purpose, we manually annotate the facial landmarks on the Carl and EURECOM datasets. The proposed approach is extensively tested on three publicly available datasets: Carl, UND-X1, and EURECOM. Experimental results show that the proposed approach improves the state-of-the-art significantly. We observe that alignment increases the performance by around 2%. Annotated facial landmark positions in this study can be downloaded from the following link: github.com/Alpkant/Thermal-to-Visible-Face-Recognition-Using-Deep-Autoencoders.

Original languageEnglish
Title of host publication2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Proceedings
EditorsBromme Bromme, Christoph Busch, Antitza Dantcheva, Christian Rathgeb, Andreas Uhl
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783885796909
Publication statusPublished - Sept 2019
Event2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Darmstadt, Germany
Duration: 18 Sept 201920 Sept 2019

Publication series

Name2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019 - Proceedings

Conference

Conference2019 International Conference of the Biometrics Special Interest Group, BIOSIG 2019
Country/TerritoryGermany
CityDarmstadt
Period18/09/1920/09/19

Bibliographical note

Publisher Copyright:
© 2019 Gesellschaft fuer Informatik.

Keywords

  • Autoencoders
  • Convolutional neural networks
  • Heterogeneous face recognition
  • Thermal to visible matching

Fingerprint

Dive into the research topics of 'Thermal to visible face recognition using deep autoencoders'. Together they form a unique fingerprint.

Cite this