FICGAN: Facial Identity Controllable GAN for De-identification
- URL: http://arxiv.org/abs/2110.00740v1
- Date: Sat, 2 Oct 2021 07:09:27 GMT
- Title: FICGAN: Facial Identity Controllable GAN for De-identification
- Authors: Yonghyun Jeong, Jooyoung Choi, Sungwon Kim, Youngmin Ro, Tae-Hyun Oh,
Doyeon Kim, Heonseok Ha, Sungroh Yoon
- Abstract summary: We present Facial Identity Controllable GAN (FICGAN) for generating high-quality de-identified face images with ensured privacy protection.
Based on the analysis, we develop FICGAN, an autoencoder-based conditional generative model that learns to disentangle the identity attributes from non-identity attributes on a face image.
- Score: 34.38379234653657
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we present Facial Identity Controllable GAN (FICGAN) for not
only generating high-quality de-identified face images with ensured privacy
protection, but also detailed controllability on attribute preservation for
enhanced data utility. We tackle the less-explored yet desired functionality in
face de-identification based on the two factors. First, we focus on the
challenging issue to obtain a high level of privacy protection in the
de-identification task while uncompromising the image quality. Second, we
analyze the facial attributes related to identity and non-identity and explore
the trade-off between the degree of face de-identification and preservation of
the source attributes for enhanced data utility. Based on the analysis, we
develop Facial Identity Controllable GAN (FICGAN), an autoencoder-based
conditional generative model that learns to disentangle the identity attributes
from non-identity attributes on a face image. By applying the manifold k-same
algorithm to satisfy k-anonymity for strengthened security, our method achieves
enhanced privacy protection in de-identified face images. Numerous experiments
demonstrate that our model outperforms others in various scenarios of face
de-identification.
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