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International Journal of Computer Vision

Erschienen in:

23.07.2016

Deep Perceptual Mapping for Cross-Modal Face Recognition

verfasst von: M. Saquib Sarfraz, Rainer Stiefelhagen

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2017

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Abstract

Cross modal face matching between the thermal and visible spectrum is a much desired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship between the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from the visible to the thermal spectrum while preserving the identity information. We show substantive performance improvement on three difficult thermal–visible face datasets. The presented approach improves the state-of-the-art by more than 10 % on the UND-X1 dataset and by more than 15–30 % on the NVESD dataset in terms of Rank-1 identification. Our method bridges the drop in performance due to the modality gap by more than 40 %.

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Titel: Deep Perceptual Mapping for Cross-Modal Face Recognition
verfasst von: M. Saquib Sarfraz
Rainer Stiefelhagen
Publikationsdatum: 23.07.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 3/2017
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0933-2

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