Deyu Lin , Huanxin Wang , Xin Lei , Weidong Min , Chenguang Yao , Yuan Zhong , Yong Liang Guan
{"title":"DSU-GAN: A robust frontal face recognition approach based on generative adversarial network","authors":"Deyu Lin , Huanxin Wang , Xin Lei , Weidong Min , Chenguang Yao , Yuan Zhong , Yong Liang Guan","doi":"10.1016/j.cviu.2024.104128","DOIUrl":null,"url":null,"abstract":"<div><div>Face recognition technology is widely used in different areas, such as entrance guard, payment <em>etc</em>. However, little attention has been given to non-positive faces recognition, especially model training and the quality of the generated images. To this end, a novel robust frontal face recognition approach based on generative adversarial network (DSU-GAN) is proposed in this paper. A mechanism of consistency loss is presented in deformable convolution proposed in the generator-encoder to avoid additional computational overhead and the problem of overfitting. In addition, a self-attention mechanism is presented in generator–encoder to avoid information overloading and construct the long-term dependencies at the pixel level. To balance the capability between the generator and discriminator, a novelf discriminator architecture based U-Net is proposed. Finally, the single-way discriminator is improved through a new up-sampling module. Experiment results demonstrate that our proposal achieves an average Rank-1 recognition rate of 95.14% on the Multi-PIE face dataset in dealing with the multi-pose. In addition, it is proven that our proposal has achieved outstanding performance in recent benchmarks conducted on both IJB-A and IJB-C.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002091","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Face recognition technology is widely used in different areas, such as entrance guard, payment etc. However, little attention has been given to non-positive faces recognition, especially model training and the quality of the generated images. To this end, a novel robust frontal face recognition approach based on generative adversarial network (DSU-GAN) is proposed in this paper. A mechanism of consistency loss is presented in deformable convolution proposed in the generator-encoder to avoid additional computational overhead and the problem of overfitting. In addition, a self-attention mechanism is presented in generator–encoder to avoid information overloading and construct the long-term dependencies at the pixel level. To balance the capability between the generator and discriminator, a novelf discriminator architecture based U-Net is proposed. Finally, the single-way discriminator is improved through a new up-sampling module. Experiment results demonstrate that our proposal achieves an average Rank-1 recognition rate of 95.14% on the Multi-PIE face dataset in dealing with the multi-pose. In addition, it is proven that our proposal has achieved outstanding performance in recent benchmarks conducted on both IJB-A and IJB-C.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems