IET Biometrics 期刊简介

IET Biometrics
英文简介:

The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding.

The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies:

Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.)
Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches
Soft biometrics and information fusion for identification, verification and trait prediction
Human factors and the human-computer interface issues for biometric systems, exception handling strategies
Template construction and template management, ageing factors and their impact on biometric systems
Usability and user-oriented design, psychological and physiological principles and system integration
Sensors and sensor technologies for biometric processing
Database technologies to support biometric systems
Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation
Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection
Biometric cryptosystems, security and biometrics-linked encryption
Links with forensic processing and cross-disciplinary commonalities
Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated
Applications and application-led considerations
Position papers on technology or on the industrial context of biometric system development
Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions
Relevant ethical and social issues

中文简介:(来自Google、百度翻译)

生物特征识别领域-基于个人的行为和生物学特征对其进行自动识别-现已达到成熟的水平,可行的实际应用既可能又越来越多。生物识别技术领域的特点尤其在于其跨学科性,因为尽管主要集中在强大的技术基础上,但有效的系统设计和实施通常需要广泛的技能,包括人为因素,数据安全和数据库技术,心理和生理意识等。此外,技术焦点本身包含多样性,因为有效的生物识别系统的工程需要图像分析,模式识别,传感器技术,数据库工程,安全设计和许多其他理解的集成。
期刊的范围是有意的相对较宽。在关注核心技术问题的同时,人们认识到这些问题可能具有内在的多样性,并且在许多情况下可能跨越传统的学科界限。因此,该期刊的范围将包括任何主题,可以证明论文可以增进我们对生物识别系统的理解,表明生物识别技术的未来发展和应用,或促进相关技术的更多实际应用:
开发和增强个人生物识别模式,包括既定和传统模式 (例如面部,指纹,虹膜,签名和手写识别) 以及更新或新兴的模式 (步态,耳朵形状,神经系统模式等)。理论和实践问题,实际系统的实现,多分类器和多模态方法
用于识别,验证和特征预测的软生物识别和信息融合
生物识别系统的人为因素和人机界面问题,异常处理策略
模板构建和模板管理,老化因素及其对生物识别系统的影响
可用性和面向用户的设计,心理和生理原理以及系统集成
用于生物识别处理的传感器和传感器技术
支持生物识别系统的数据库技术
生物识别系统的实施,安全工程含义,智能卡和相关技术在实现、实现平台、系统设计和性能评估
信任和隐私问题、生物识别系统的安全性和支持技术解决方案、生物识别模板保护
生物识别密码系统、与安全和生物识别相关的加密
与取证处理和跨学科共性的链接
核心基础技术 (例如图像分析,模式识别,计算机视觉,信号处理等),可以证明与生物识别处理的特定相关性
应用和应用主导的考虑事项
关于技术或生物识别系统开发的工业背景的立场文件
生物识别标准的采用和推广,提高技术接受度,部署和互操作性,避免跨文化及跨部门限制
相关伦理及社会议题

期刊ISSN
2047-4938
影响指数
2.562
最新CiteScore值
6.40 查看CiteScore评价数据
最新自引率
10.50%
官方指定润色网址
https://www.deeredit.com/?type=ss1
投稿语言要求

Improve the quality of the paper, eliminate grammar and spelling errors, increase readability, ensure accurate communication of viewpoints, enhance academic reputation, and increase the chances of the paper being accepted.

建议点击这个网址:https://www.deeredit.com/?type=ss2,资深审稿专家为您评估稿件质量,提供针对性改进建议,最终可助您极大提升目标期刊录用率

期刊官方网址

hot

https://www.peipusci.com/?type=9
杂志社征稿网址

hot

https://www.peipusci.com/?type=10
通讯地址
WILEY, 111 RIVER ST, HOBOKEN, USA, NJ, 07030-5774
偏重的研究方向(学科)
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
出版周期
Bi-monthly
出版年份
2012
出版国家/地区
USA
是否OA
Yes
SCI期刊coverage
Science Citation Index Expanded(科学引文索引扩展)
NCBI查询
PubMed Central (PMC)链接 全文检索(pubmed central)
IET Biometrics 期刊中科院JCR 评价数据
最新中科院JCR分区
大类(学科)
小类(学科)
综述期刊
工程技术
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(计算机:人工智能)4区
最新的影响因子
2.562
最新公布的期刊年发文量
年度总发文量 研究类文章占比
37 83.78%
总被引频次 19
影响因子趋势图
近年的影响因子趋势图(整体平稳趋势)

2022年预警名单预测最新

IET Biometrics 期刊CiteScore评价数据
最新CiteScore值
6.40
年文章数 37
SJR
0.434
SNIP
1.396
CiteScore排名
序号 类别(学科) 排名 百分位
1 Computer Science Software #88/389
2 Computer Science Signal Processing #25/108
3 Computer Science Computer Vision and Pattern Recognition #20/85
CiteScore趋势图
CiteScore趋势图
IET Biometrics 投稿经验(由下方点评分析获得,1人参与,2015人阅读)
投稿录用比例: 0.001000
审稿速度: 33 Weeks
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