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The paper presents an approach to multimodal image registration. The method is developed for aligning infrared (IR) and visual (RGB) images of facades. It is based on mapping clouds of points extracted by a corner detector applied to both images. The experiments show that corners are suitable features for our application. In the alignment process a number of transformation hypotheses is generated and evaluated. The evaluation is performed by measuring similarity between the RGB corners and the transformed corners from IR image. Directed partial Hausdorff distance is used as a robust similarity measure. The implemented system has been tested on various IR-RGB pairs of images of buildings. The results show that the method can be used for image registration, but also expose some typical problems.

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The authors introduce a system for person de-identification in video data that de-identifies biometric and nonbiometric features, namely faces, hairstyles and clothing colours. The authors' system detects human faces and silhouettes in the input video and replaces the detected faces with random synthesised faces obtained using a deep convolutional generative adversarial network. Alternative hairstyles are rendered over the synthesised faces, and the human silhouette is recoloured so that skin hues are preserved and clothing hues are altered. Through the use of artificially synthesised faces that look realistic, they ensure that the de-identified image looks natural and at the same time avoid ethical and legal considerations present when using real face images as replacement faces. As they address non-biometric feature de-identification, their system offers a considerably higher level of privacy protection than commonly employed solutions that use simple image processing techniques such as blurring. Qualitative and quantitative evaluation suggests that their system produces de-identified images that look natural, at the same time being resistant to re-identification attacks.

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