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.
Converting Thermal Infrared Face Images into Normal Gray-Level Images
Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007
2007-01-01
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Face Recognition , Facial Image , Canonical Correlation Analysis , Canonical Variate , Infrared Image Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence , Biometrics , Algorithm Analysis and Problem Complexity
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