Iris deidentification
Source code: [GitHub]
(Under construction...)
The very high recognition accuracy of iris-based biometric systems and the increasing
distribution of high-resolution personal images on websites and social media are creating privacy risks that
users and the biometric community have not yet addressed properly. Biometric information contained in the
iris region can be used to automatically recognize individuals even after several years, potentially enabling
pervasive identification, recognition, and tracking of individuals without explicit consent. To address this
issue, this paper presents two main contributions. First, we demonstrate, through practical examples, that
the risk associated with iris-based identification by means of images collected from public websites and
social media is real. Second, we propose an innovative method based on generative adversarial networks
(GANs) that can automatically generate novel images with high visual realism, in which all the biometric
information associated with an individual in the iris region has been removed and replaced. We tested the
proposed method on an image dataset composed of high-resolution portrait images collected from the web.
The results show that the generated deidentified images significantly reduce the privacy risks and, in most
cases, are indistinguishable from real samples.
Examples
Examples of faces with irises deidentified using the proposed approach (face image).
The proposed method for iris deidentification generates images with high visual realism. |
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Examples of images with irises deidentified using the proposed approach (only iris region).
The proposed method for iris deidentification generates images with high visual realism. |
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Project page: https://iebil.di.unimi.it/irisGan/irisGan.html