Face alignment by learning from small real datasets and large synthetic datasets

Haoqi Gao, K. Ogawara
{"title":"Face alignment by learning from small real datasets and large synthetic datasets","authors":"Haoqi Gao, K. Ogawara","doi":"10.1109/CACML55074.2022.00073","DOIUrl":null,"url":null,"abstract":"In recent years, face-related research had a wide variety of real-life applications. However, issues such as privacy violations and data abuse caused by its applications have also triggered global controversy. It is undeniable that face-related technology is efficient and convenient, but the dangers and risks are hidden by face technology should be comprehensively considered. Current face algorithms are still challenging in complex and challenging environments (e.g., large angles or expressions). Firstly the existing public training datasets are mostly frontal faces, which have an unbalanced distribution of challenging data. Secondly, the collected real datasets require explicit user consent, and the annotation process is time-consuming and expensive. In this paper, we open a new research direction through synthetic datasets. We try to use synthetic datasets to reduce the dependence of the model on the real-world data set. The face alignment experiments explore the synthetic dataset's complementarity and availability.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CACML55074.2022.00073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In recent years, face-related research had a wide variety of real-life applications. However, issues such as privacy violations and data abuse caused by its applications have also triggered global controversy. It is undeniable that face-related technology is efficient and convenient, but the dangers and risks are hidden by face technology should be comprehensively considered. Current face algorithms are still challenging in complex and challenging environments (e.g., large angles or expressions). Firstly the existing public training datasets are mostly frontal faces, which have an unbalanced distribution of challenging data. Secondly, the collected real datasets require explicit user consent, and the annotation process is time-consuming and expensive. In this paper, we open a new research direction through synthetic datasets. We try to use synthetic datasets to reduce the dependence of the model on the real-world data set. The face alignment experiments explore the synthetic dataset's complementarity and availability.
从小型真实数据集和大型合成数据集学习人脸对齐
近年来,与面部相关的研究在现实生活中有着广泛的应用。然而,其应用程序引发的隐私侵犯和数据滥用等问题也引发了全球争议。不可否认的是,人脸相关技术的高效和便捷,但人脸技术所隐藏的危险和风险需要综合考虑。当前的人脸算法在复杂和具有挑战性的环境中(例如,大角度或表情)仍然具有挑战性。首先,现有的公共训练数据集多为正面人脸,具有挑战性的数据分布不平衡。其次,收集的真实数据集需要明确的用户同意,并且标注过程耗时且昂贵。在本文中,我们通过合成数据集开辟了一个新的研究方向。我们尝试使用合成数据集来减少模型对真实数据集的依赖。人脸对齐实验探索了合成数据集的互补性和可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信