GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
CoRR(2023)
摘要
In recent years, the increasing availability of personal data has raised
concerns regarding privacy and security. One of the critical processes to
address these concerns is data anonymization, which aims to protect individual
privacy and prevent the release of sensitive information. This research focuses
on the importance of face anonymization. Therefore, we introduce
GANonymization, a novel face anonymization framework with facial
expression-preserving abilities. Our approach is based on a high-level
representation of a face, which is synthesized into an anonymized version based
on a generative adversarial network (GAN). The effectiveness of the approach
was assessed by evaluating its performance in removing identifiable facial
attributes to increase the anonymity of the given individual face.
Additionally, the performance of preserving facial expressions was evaluated on
several affect recognition datasets and outperformed the state-of-the-art
methods in most categories. Finally, our approach was analyzed for its ability
to remove various facial traits, such as jewelry, hair color, and multiple
others. Here, it demonstrated reliable performance in removing these
attributes. Our results suggest that GANonymization is a promising approach for
anonymizing faces while preserving facial expressions.
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关键词
face ganonymization framework,preserving
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