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.


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    Title :

    Converting Thermal Infrared Face Images into Normal Gray-Level Images


    Contributors:
    Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Dou, Mingsong (author) / Zhang, Chao (author) / Hao, Pengwei (author) / Li, Jun (author)

    Conference:

    Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007


    Published in:

    Computer Vision – ACCV 2007 ; Chapter : 71 ; 722-732


    Publication date :

    2007-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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