[视觉算法] 【求助帖】Face De-Identification

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工程Jellyfish
2016-1-6 12:18:03 显示全部楼层

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With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. In most of these applications knowledge of the identity of people in the image is not required. This makes the case for image de-identification, the removal of identifying information from images, prior to sharing of the data. Privacy protection methods are well established for field-structured data, however, work on images is still limited. In this chapter we review previously proposed na¨ıve and formal face de-identification methods. We then describe a novel framework for the deidentification of face images using multi-factor models which unify linear, bilinear, and quadratic data models. We show in experiments on a large expression-variant face database that the new algorithm is able to protect privacy while preserving data utility. The new model extends directly to image sequences, which we demonstrate on examples from a medical face database.

Face De-identification.pdf

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[视觉算法] 【求助帖】Face De-Identification

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前哨站012地下党
2016-1-8 14:32:49 显示全部楼层
随着新应用的出现,围绕图像数据的共享,在现场出现的人的隐私保护的问题。在这些应用中,人们对图像中的人的身份的认识是不需要的。这使得图像去识别的情况下,去除识别信息的图像,之前的数据共享。隐私保护的方法是建立在现场的数据,但是,在图像上的工作仍然是有限的。在这一章中,我们回顾以前提出的那¨ıVE和正式的脸部识别方法。我们描述了一个新的框架,采用多因素模型,结合线性、双线性人脸图像的deidentification,和二次数据模型。在实验中,我们在一个大的表达式的变体人脸数据库,新的算法是能够保护隐私,同时保留数据实用程序。新的模型直接延伸到图像序列,我们展示了从医学人脸数据库的例子。

(来自百度翻译)
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