Intricate Face Recognition Based On Virtual Sample Generation

Abstract

This paper mainly focuses on the problem of low recognition rate and making deep hidden features unable to learn caused by the lack of training samples in intricate facial recognition, a method of virtual image sample generation is proposed. The starGAN network based on style migration is used to generate the image that does not exist in the original database, and multiple images with different facial attributes are added to a single sample, combine the obtained virtual samples with the original database to build a larger face database for face recognition. Experiments on the CelebA face database show that the proposed method can effectively improve the face recognition rate in complex situations.