Open AccessIntricate Face Recognition Based On Virtual Sample Generation
Author(s) -
Qionglin Tong,
Jinshan Su,
Li Ma,
Zhenhua Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1992/4/042037
Subject(s) - computer science , facial recognition system , artificial intelligence , face (sociological concept) , three dimensional face recognition , pattern recognition (psychology) , sample (material) , image (mathematics) , computer vision , face detection , social science , chemistry , chromatography , sociology
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.