Reconstruct and De-identify (RaD): A Joint Task Framework for Face Reconstruction and De-identification Leveraging the 3D Morphable Model Explainability

Allam Shehata, Mohamad Ammar Alsherfawi Aljazaerly, Yasushi Yagi

Abstract: Current face de-identification methods often struggle to balance robust privacy protection with preserving image utility. While existing approaches effectively obscure identity, they frequently degrade visual quality, limiting their practical applicability. To address this challenge, we propose a robust reconstruction and de-identification framework (RaD) that leverages the 3D Morphable Model (3DMM) explicit representation. We first utilize a CNN encoder to predict the disentangled 3DMM coefficients, enabling a coarse reconstruction via a differentiable renderer. To enrich facial detail beyond the 3DMM’s topology and statistical priors, we introduce a personalized albedo generator (PAG) that adds fine texture details. For de-identification, we then apply a transformer‑based identity protector (IP) to manipulate the 3DMM shape and texture parameters in order to conceal identity‑sensitive features while preserving the photorealism of the reconstructed images. Finally, an Image Enhancement Module (IEM) refines the outputs, removing any artifacts and further enhancing visual quality. Extensive experiments on multiple benchmarks demonstrate that our framework outperforms state‑of‑the‑art methods both quantitatively and qualitatively, making it well-suited for applications that require realistic reconstructions along with privacy preservation without compromising image quality.