Iris Deidentification With High Visual Realism for Privacy Protection on Websites and Social Networks

• Main Authors: Mauro Barni, Ruggero Donida Labati, Angelo Genovese, Vincenzo Piuri, Fabio Scotti
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Biometrics; deidentification; GAN; iris; privacy
• Online Access: https://ieeexplore.ieee.org/document/9543669/

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.