Benchmarking 3D Face De-Identification with Preserving Facial Attributes

Privacy with the use of face images is becoming a major concern in civilians’ applications. Recent studies have exploited privacy protection methods by means of facial attributes editing or de-identifying face images. Altering attributes causes loss of information for facial analysis while most de-identification studies did not quantitatively evaluate how well facial attributes are preserved. Moreover, state-of-the-art face analysis utilized 3D information for better performance. Existing face privacy studies only focusing in 2D domain is a key limitation towards the compatibility of more advanced 3D face analysis. This paper presents the first study on the possibility of 3D face de-identification with preserving facial attributes. We systematically evaluate the performance of 2D/3D face/facial attribute recognition and develop 2D/3D de-identification methods with preserving facial attributes using Auto Encoder and Generative Adversarial Networks approaches. We present comprehensive and reproducible experimental results using a publicly available 3D face database with facial attribute annotations for benchmarking and further research. https://github.com/kevinhmcheng/3d-face-de-id