Deep Perceptual Mapping for Cross-Modal Face Recognition

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...

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Bibliographic Details
Published in:International journal of computer vision 2017-05, Vol.122 (3), p.426-438
Main Authors: Sarfraz, M. Saquib, Stiefelhagen, Rainer
Format: Article
Language:English
Subjects:
Access control
Analysis
Artificial Intelligence
Artificial neural networks
Biometry
Computer Imaging
Computer Science
Datasets
Discriminant analysis
Face
Face recognition
Facial recognition technology
Image Processing and Computer Vision
Mapping
Matching
Methods
Neural networks
Nonlinearity
Pattern Recognition
Pattern Recognition and Graphics
Regression analysis
Studies
Surveillance
Visible spectrum
Vision
Vision systems
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Summary: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 %.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-016-0933-2