合成人脸老化:年龄稳健人脸识别算法的评估、分析与促进

Wang Yao;Muhammad Ali Farooq;Joseph Lemley;Peter Corcoran
{"title":"合成人脸老化:年龄稳健人脸识别算法的评估、分析与促进","authors":"Wang Yao;Muhammad Ali Farooq;Joseph Lemley;Peter Corcoran","doi":"10.1109/TBIOM.2025.3536622","DOIUrl":null,"url":null,"abstract":"Establishing the identity of an individual from their facial data is widely adopted across the consumer sector, driven by the use of facial authentication on handheld devices. This widespread use of facial authentication technology has raised other issues, in particular those of biases in the underlying algorithms. Initial studies focused on ethnic or gender biases, but another area is that of age-related biases. This research work focuses on the challenge of face recognition over decades-long time intervals and explores the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models in recognizing people across these longer time intervals. To achieve this, we first design a set of experiments to evaluate state-of-the-art synthetic ageing methods. In the next stage, we explore the effect of age intervals on a reference face recognition algorithm using both synthetic and real ageing data to perform rigorous validation. We then use these synthetic age data as an augmentation method to facilitate the age-invariant face recognition algorithm. Extensive experimental results demonstrate a notable improvement in the recognition rate of the model trained on synthetic ageing images, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap. Additionally, our models exhibit competitive performance when validated on benchmark cross-age datasets and general face recognition datasets. These findings underscore the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 3","pages":"471-483"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858190","citationCount":"0","resultStr":"{\"title\":\"Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms\",\"authors\":\"Wang Yao;Muhammad Ali Farooq;Joseph Lemley;Peter Corcoran\",\"doi\":\"10.1109/TBIOM.2025.3536622\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Establishing the identity of an individual from their facial data is widely adopted across the consumer sector, driven by the use of facial authentication on handheld devices. This widespread use of facial authentication technology has raised other issues, in particular those of biases in the underlying algorithms. Initial studies focused on ethnic or gender biases, but another area is that of age-related biases. This research work focuses on the challenge of face recognition over decades-long time intervals and explores the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models in recognizing people across these longer time intervals. To achieve this, we first design a set of experiments to evaluate state-of-the-art synthetic ageing methods. In the next stage, we explore the effect of age intervals on a reference face recognition algorithm using both synthetic and real ageing data to perform rigorous validation. We then use these synthetic age data as an augmentation method to facilitate the age-invariant face recognition algorithm. Extensive experimental results demonstrate a notable improvement in the recognition rate of the model trained on synthetic ageing images, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap. Additionally, our models exhibit competitive performance when validated on benchmark cross-age datasets and general face recognition datasets. These findings underscore the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.\",\"PeriodicalId\":73307,\"journal\":{\"name\":\"IEEE transactions on biometrics, behavior, and identity science\",\"volume\":\"7 3\",\"pages\":\"471-483\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858190\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on biometrics, behavior, and identity science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10858190/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biometrics, behavior, and identity science","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10858190/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在手持设备上使用面部认证的推动下,通过面部数据建立个人身份在消费领域被广泛采用。面部认证技术的广泛使用引发了其他问题,特别是底层算法中的偏见。最初的研究集中在种族或性别偏见上,但另一个领域是与年龄相关的偏见。本研究工作聚焦于数十年时间间隔的人脸识别挑战,并探索利用合成老化数据提高人脸识别模型在这些更长的时间间隔中识别人物的鲁棒性的可行性。为了实现这一目标,我们首先设计了一套实验来评估最先进的合成老化方法。在下一阶段,我们将探索年龄间隔对参考人脸识别算法的影响,使用合成和真实的老化数据进行严格的验证。然后,我们使用这些合成的年龄数据作为增强方法来促进年龄不变人脸识别算法。大量的实验结果表明,该模型在人工老化图像上的识别率有了显著提高,在40岁年龄差图像上的识别率比基线模型提高了3.33%。此外,当在基准跨年龄数据集和一般人脸识别数据集上进行验证时,我们的模型显示出具有竞争力的性能。这些发现强调了合成年龄数据在提高年龄不变人脸识别系统性能方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms
Establishing the identity of an individual from their facial data is widely adopted across the consumer sector, driven by the use of facial authentication on handheld devices. This widespread use of facial authentication technology has raised other issues, in particular those of biases in the underlying algorithms. Initial studies focused on ethnic or gender biases, but another area is that of age-related biases. This research work focuses on the challenge of face recognition over decades-long time intervals and explores the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models in recognizing people across these longer time intervals. To achieve this, we first design a set of experiments to evaluate state-of-the-art synthetic ageing methods. In the next stage, we explore the effect of age intervals on a reference face recognition algorithm using both synthetic and real ageing data to perform rigorous validation. We then use these synthetic age data as an augmentation method to facilitate the age-invariant face recognition algorithm. Extensive experimental results demonstrate a notable improvement in the recognition rate of the model trained on synthetic ageing images, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap. Additionally, our models exhibit competitive performance when validated on benchmark cross-age datasets and general face recognition datasets. These findings underscore the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
10.90
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