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Thermal to Visible Face Recognition Using Deep Autoencoders

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dc.contributor.authorKantarcı, Alperen
dc.contributor.authorEkenel, Hazım Kemal
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-15T13:01:28Z
dc.date.available2020-09-15T13:01:28Z
dc.date.issued2019
dc.description.abstractVisible 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 .en
dc.identifier.isbn978-3-88579-690-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34233
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-297
dc.subjectConvolutional neural networks
dc.subjectautoencoders
dc.subjectheterogeneous face recognition
dc.subjectthermal to visible matching
dc.titleThermal to Visible Face Recognition Using Deep Autoencodersen
dc.typeText/Conference Paper
gi.citation.endPage220
gi.citation.publisherPlaceBonn
gi.citation.startPage213
gi.conference.date18.-20. September 2019
gi.conference.locationDarmstadt, Germany
gi.conference.sessiontitleFurther Conference Contributions

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P296 - BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group