3D Face Morph Generation Using Geometry-Aware Template Inversion

While face recognition systems have become a popular solution for applications which require automatic authentication, their vulnerability to morphing attacks has become a major concern in sensitive scenarios. This work proposes a novel method to generate 3D face morphs. Given two source images, we use their face embeddings to derive an optimal morph embedding, and then use a geometry-aware template inversion method based on Generative Neural Radiance Fields (GNeRF) to construct a 3D face morph from this optimal embedding. Leveraging from the GNeRF structure, we can generate morph images with any arbitrary view-point. Our experiments show that our method achieve comparable performance with previous morph generation methods from the literature, and has an additional advantage of generating 3D results. To our knowledge, this is the first work on generating 3D face morphs based on GNeRF models, and it can potentially be used for sophisticated morphing attacks. The source code of our experiments is publicly released.