Towards Large-Pose Face Frontalization in the Wild

Understand

Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments.

• Learning pose-invariant features is one solution, but needs expensively labeled large-scale data and carefully designed feature learning algorithms.
• In this work, we focus on frontalizing faces in the wild under various head poses, including extreme profile views.
• We propose a novel deep 3D Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial Network (GAN), termed as FF-GAN, to generate neutral head pose face images.