Multi-View Normalization For Face Recognition
Chia-Hao Tang, Yi-Mei Chou, Gee-Sern Jison Hsu · 2021
Unlike the majority of face normalization that focuses on single-view frontalization, we propose the Multi-View Normalization (MVN) framework to normalize an arbitrary face to multiple desired poses with balanced illumination and neutral expression. Taking the advantages of generative and adversarial learning, the proposed MVN transforms a face into a set of multi-view faces with facial identity well preserved, offering a better representation to handle face recognition. The MVN is designed to learn the transformation from a input set to seven output sets. The input set contains faces collected in the wild with arbitrary poses, lighting conditions and expressions. The seven output sets include seven poses from $0^{\circ}\sim 90^{\circ}$ in yaw with $15^{\mathrm{\circ}}$ interval with balanced illumination and neutral expression. The MVN is composed of one face encoder, seven pose-specific generators and seven sets of discriminators. The encoder is made of a face recognition expert network, which is not updated during training and acts as a facial feature extractor. The generators are trained to transform the input set to the seven output sets. The discriminators are trained to not only ensure the photo-realistic quality of the generated faces, but also force the poses of the generated faces to the desired poses with corresponding facial appearances. Experiments on several benchmark datasets show that the proposed MVN demonstrates a competitive performance to state-of-the-art approaches.