  A wealth of angle problems occur when facial recognition is performed: At
present, the feature extraction network presents eigenvectors with large
differences between the frontal face and profile face recognition of the same
person in many cases. For this reason, the state-of-the-art facial recognition
network will use multiple samples for the same target to ensure that
eigenvector differences caused by angles are ignored during training. However,
there is another solution available, which is to generate frontal face images
with profile face images before recognition. In this paper, we proposed a
method of generating frontal faces with image-to-image profile faces based on
Generative Adversarial Network (GAN).
