规范化全手掌:迈向更准确的基于手的多模态生物识别

IF 18.6
Yitao Qiao;Wenxiong Kang;Dacan Luo;Junduan Huang
{"title":"规范化全手掌:迈向更准确的基于手的多模态生物识别","authors":"Yitao Qiao;Wenxiong Kang;Dacan Luo;Junduan Huang","doi":"10.1109/TPAMI.2025.3564514","DOIUrl":null,"url":null,"abstract":"Hand-based multimodal biometrics have attracted significant attention due to their high security and performance. However, existing methods fail to adequately decouple various hand biometric traits, limiting the extraction of unique features. Moreover, effective feature extraction for multiple hand traits remains a challenge. To address these issues, we propose a novel method for the precise decoupling of hand multimodal features called ‘Normalized-Full-Palmar-Hand’ and construct an authentication system based on this method. First, we propose HSANet, which accurately segments various hand regions with diverse backgrounds based on low-level details and high-level semantic information. Next, we establish two hand multimodal biometric databases with HSANet: SCUT Normalized-Full-Palmar-Hand Database Version 1 (SCUT_NFPH_v1) and Version 2 (SCUT_NFPH_v2). These databases include full hand images, semantic masks, and images of various hand biometric traits obtained from the same individual at the same scale, totaling 157,500 images. Third, we propose the Full Palmar Hand Authentication Network framework (FPHandNet) to extract unique features of multiple hand biometric traits. Finally, extensive experimental results, performed via the publicly available CASIA, IITD, COEP databases, and our proposed databases, validate the effectiveness of our methods.","PeriodicalId":94034,"journal":{"name":"IEEE transactions on pattern analysis and machine intelligence","volume":"47 8","pages":"6715-6730"},"PeriodicalIF":18.6000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Normalized-Full-Palmar-Hand: Toward More Accurate Hand-Based Multimodal Biometrics\",\"authors\":\"Yitao Qiao;Wenxiong Kang;Dacan Luo;Junduan Huang\",\"doi\":\"10.1109/TPAMI.2025.3564514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hand-based multimodal biometrics have attracted significant attention due to their high security and performance. However, existing methods fail to adequately decouple various hand biometric traits, limiting the extraction of unique features. Moreover, effective feature extraction for multiple hand traits remains a challenge. To address these issues, we propose a novel method for the precise decoupling of hand multimodal features called ‘Normalized-Full-Palmar-Hand’ and construct an authentication system based on this method. First, we propose HSANet, which accurately segments various hand regions with diverse backgrounds based on low-level details and high-level semantic information. Next, we establish two hand multimodal biometric databases with HSANet: SCUT Normalized-Full-Palmar-Hand Database Version 1 (SCUT_NFPH_v1) and Version 2 (SCUT_NFPH_v2). These databases include full hand images, semantic masks, and images of various hand biometric traits obtained from the same individual at the same scale, totaling 157,500 images. Third, we propose the Full Palmar Hand Authentication Network framework (FPHandNet) to extract unique features of multiple hand biometric traits. Finally, extensive experimental results, performed via the publicly available CASIA, IITD, COEP databases, and our proposed databases, validate the effectiveness of our methods.\",\"PeriodicalId\":94034,\"journal\":{\"name\":\"IEEE transactions on pattern analysis and machine intelligence\",\"volume\":\"47 8\",\"pages\":\"6715-6730\"},\"PeriodicalIF\":18.6000,\"publicationDate\":\"2025-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on pattern analysis and machine intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10978896/\",\"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 pattern analysis and machine intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10978896/","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

基于手部的多模态生物识别技术因其高安全性和高性能而备受关注。然而,现有的方法不能充分解耦各种手部生物特征,限制了独特特征的提取。此外,多手特征的有效提取仍然是一个挑战。为了解决这些问题,我们提出了一种新的手部多模态特征精确解耦方法,称为“归一化-全手掌-手部”,并基于该方法构建了一个认证系统。首先,我们提出了基于低层次细节和高层次语义信息的HSANet方法,该方法可以准确地分割出不同背景的手区域。接下来,我们用HSANet建立了两个手部多模态生物特征数据库:SCUT规范化全手掌数据库版本1 (SCUT_NFPH_v1)和版本2 (SCUT_NFPH_v2)。这些数据库包括全手图像、语义掩模和同一个体在相同尺度下获得的各种手部生物特征图像,共计157,500张图像。第三,我们提出了全手掌认证网络框架(FPHandNet)来提取多个手部生物特征的独特特征。最后,通过公开的CASIA, IITD, COEP数据库和我们提出的数据库进行的大量实验结果验证了我们方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Normalized-Full-Palmar-Hand: Toward More Accurate Hand-Based Multimodal Biometrics
Hand-based multimodal biometrics have attracted significant attention due to their high security and performance. However, existing methods fail to adequately decouple various hand biometric traits, limiting the extraction of unique features. Moreover, effective feature extraction for multiple hand traits remains a challenge. To address these issues, we propose a novel method for the precise decoupling of hand multimodal features called ‘Normalized-Full-Palmar-Hand’ and construct an authentication system based on this method. First, we propose HSANet, which accurately segments various hand regions with diverse backgrounds based on low-level details and high-level semantic information. Next, we establish two hand multimodal biometric databases with HSANet: SCUT Normalized-Full-Palmar-Hand Database Version 1 (SCUT_NFPH_v1) and Version 2 (SCUT_NFPH_v2). These databases include full hand images, semantic masks, and images of various hand biometric traits obtained from the same individual at the same scale, totaling 157,500 images. Third, we propose the Full Palmar Hand Authentication Network framework (FPHandNet) to extract unique features of multiple hand biometric traits. Finally, extensive experimental results, performed via the publicly available CASIA, IITD, COEP databases, and our proposed databases, validate the effectiveness of our methods.
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
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