Converting thermal infrared face images into normal gray-level images

Authors:
Mingsong Dou;Chao Zhang;Pengwei Hao;Jun Li
Affiliations:
State Key Laboratory of Machine Perception, Peking University, Beijing, China;State Key Laboratory of Machine Perception, Peking University, Beijing, China;State Key Laboratory of Machine Perception, Peking University, Beijing, China and Department of Computer Science, Queen Mary, University of London, UK;Department of Computer Science, Queen Mary, University of London, UK
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Year:
2007

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Abstract

In this paper, we address the problem of producing visible spectrum facial images as we normally see by using thermal infrared images. We apply Canonical Correlation Analysis (CCA) to extract the features, converting a many-to-many mapping between infrared and visible images into a one-to-one mapping approximately. Then we learn the relationship between two feature spaces in which the visible features are inferred from the corresponding infrared features using Locally-Linear Regression (LLR) or, what is called, Sophisticated LLE, and a Locally Linear Embedding (LLE) method is used to recover a visible image from the inferred features, recovering some information lost in the infrared image. Experiments demonstrate that our method maintains the global facial structure and infers many local facial details from the thermal infrared images.