Recover Canonical-View Faces in the Wild with Deep Neural Networks

Understand

Face images in the wild undergo large intra-personal variations, such as poses, illuminations, occlusions, and low resolutions, which cause great challenges to face-related applications.

• This paper addresses this challenge by proposing a new deep learning framework that can recover the canonical view of face images.
• It dramatically reduces the intra-person variances, while maintaining the inter-person discriminativeness.
• Unlike the existing face reconstruction methods that were either evaluated in controlled 2D environment or employed 3D information, our approach directly learns the transformation from the face images with a complex set of variations to their canonical views.