Journal Title:Iet Biometrics
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
生物特征識(shí)別領(lǐng)域(基于個(gè)人的行為和生物特征自動(dòng)識(shí)別個(gè)人)現(xiàn)已達(dá)到成熟水平,可行的實(shí)際應(yīng)用不僅可能而且越來(lái)越可用。生物特征識(shí)別領(lǐng)域的特點(diǎn)是其跨學(xué)科性,因?yàn)殡m然主要關(guān)注強(qiáng)大的技術(shù)基礎(chǔ),但有效的系統(tǒng)設(shè)計(jì)和實(shí)施通常需要廣泛的技能,例如人為因素、數(shù)據(jù)安全和數(shù)據(jù)庫(kù)技術(shù)、心理和生理意識(shí)等。此外,技術(shù)重點(diǎn)本身包含多樣性,因?yàn)橛行У纳锾卣髯R(shí)別系統(tǒng)的工程需要整合圖像分析、模式識(shí)別、傳感器技術(shù)、數(shù)據(jù)庫(kù)工程、安全設(shè)計(jì)和許多其他理解。
該期刊的范圍故意相對(duì)較廣。雖然重點(diǎn)關(guān)注核心技術(shù)問(wèn)題,但人們認(rèn)識(shí)到這些問(wèn)題可能本質(zhì)上是多樣化的,在許多情況下可能跨越傳統(tǒng)的學(xué)科界限。因此,該期刊的范圍將包括任何可以證明論文可以增加我們對(duì)生物識(shí)別系統(tǒng)的理解、預(yù)示生物識(shí)別未來(lái)發(fā)展和應(yīng)用或促進(jìn)相關(guān)技術(shù)更廣泛實(shí)際應(yīng)用的主題:
開(kāi)發(fā)和增強(qiáng)單個(gè)生物識(shí)別模式,包括既定和傳統(tǒng)模式(例如面部、指紋、虹膜、簽名和手寫識(shí)別)以及較新或新興的模式(步態(tài)、耳朵形狀、神經(jīng)模式等)
多生物識(shí)別、理論和實(shí)踐問(wèn)題、實(shí)用系統(tǒng)的實(shí)施、多分類器和多模式方法
用于識(shí)別、驗(yàn)證和特征預(yù)測(cè)的軟生物識(shí)別和信息融合
生物識(shí)別系統(tǒng)的人為因素和人機(jī)界面問(wèn)題、異常處理策略
模板構(gòu)建和模板管理、老化因素及其對(duì)生物識(shí)別系統(tǒng)的影響
可用性和面向用戶的設(shè)計(jì)、心理和生理原理和系統(tǒng)集成
用于生物特征識(shí)別處理的傳感器和傳感器技術(shù)
支持生物特征識(shí)別系統(tǒng)的數(shù)據(jù)庫(kù)技術(shù)
生物特征識(shí)別系統(tǒng)的實(shí)施、安全工程影響、智能卡及相關(guān)實(shí)施技術(shù)、實(shí)施平臺(tái)、系統(tǒng)設(shè)計(jì)和性能評(píng)估
信任和隱私問(wèn)題、生物特征識(shí)別系統(tǒng)的安全性和支持技術(shù)解決方案、生物特征識(shí)別模板保護(hù)
生物特征識(shí)別密碼系統(tǒng)、安全性和與生物特征識(shí)別相關(guān)的加密
與法醫(yī)處理的聯(lián)系和跨學(xué)科共性
核心基礎(chǔ)技術(shù)(例如生物識(shí)別技術(shù)(例如,圖像分析、模式識(shí)別、計(jì)算機(jī)視覺(jué)、信號(hào)處理等)與生物識(shí)別處理的具體相關(guān)性可得到證明。
應(yīng)用和應(yīng)用主導(dǎo)的考慮
關(guān)于生物識(shí)別系統(tǒng)開(kāi)發(fā)的技術(shù)或工業(yè)背景的立場(chǎng)文件
采用和推廣生物識(shí)別標(biāo)準(zhǔn),提高技術(shù)接受度、部署和互操作性,避免跨文化和跨部門限制
相關(guān)的倫理和社會(huì)問(wèn)題
Iet Biometrics創(chuàng)刊于2012年,由Wiley出版商出版,收稿方向涵蓋COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE全領(lǐng)域,此期刊水平偏中等偏靠后,在所屬細(xì)分領(lǐng)域中專業(yè)影響力一般,過(guò)審相對(duì)較易,如果您文章質(zhì)量佳,選擇此期刊,發(fā)表機(jī)率較高。平均審稿速度 33 Weeks ,影響因子指數(shù)1.8,該期刊近期沒(méi)有被列入國(guó)際期刊預(yù)警名單,廣大學(xué)者值得一試。
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
計(jì)算機(jī)科學(xué) | 4區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
名詞解釋:
中科院分區(qū)也叫中科院JCR分區(qū),基礎(chǔ)版分為13個(gè)大類學(xué)科,然后按照各類期刊影響因子分別將每個(gè)類別分為四個(gè)區(qū),影響因子5%為1區(qū),6%-20%為2區(qū),21%-50%為3區(qū),其余為4區(qū)。
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
計(jì)算機(jī)科學(xué) | 3區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
計(jì)算機(jī)科學(xué) | 3區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
工程技術(shù) | 4區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
計(jì)算機(jī)科學(xué) | 3區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
大類學(xué)科 | 分區(qū) | 小類學(xué)科 | 分區(qū) | Top期刊 | 綜述期刊 |
計(jì)算機(jī)科學(xué) | 4區(qū) | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 計(jì)算機(jī):人工智能 | 4區(qū) | 否 | 否 |
按JIF指標(biāo)學(xué)科分區(qū) | 收錄子集 | 分區(qū) | 排名 | 百分位 |
學(xué)科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q3 | 136 / 197 |
31.2% |
按JCI指標(biāo)學(xué)科分區(qū) | 收錄子集 | 分區(qū) | 排名 | 百分位 |
學(xué)科:COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SCIE | Q4 | 152 / 198 |
23.48% |
名詞解釋:
WOS即Web of Science,是全球獲取學(xué)術(shù)信息的重要數(shù)據(jù)庫(kù),Web of Science包括自然科學(xué)、社會(huì)科學(xué)、藝術(shù)與人文領(lǐng)域的信息,來(lái)自全世界近9,000種最負(fù)盛名的高影響力研究期刊及12,000多種學(xué)術(shù)會(huì)議多學(xué)科內(nèi)容。給期刊分區(qū)時(shí)會(huì)按照某一個(gè)學(xué)科領(lǐng)域劃分,根據(jù)這一學(xué)科所有按照影響因子數(shù)值降序排名,然后平均分成4等份,期刊影響因子值高的就會(huì)在高分區(qū)中,最后的劃分結(jié)果分別是Q1,Q2,Q3,Q4,Q1代表質(zhì)量最高。
CiteScore | SJR | SNIP | CiteScore排名 | ||||||||||||||||
5.9 | 0.583 | 0.957 |
|
名詞解釋:
CiteScore:衡量期刊所發(fā)表文獻(xiàn)的平均受引用次數(shù)。
SJR:SCImago 期刊等級(jí)衡量經(jīng)過(guò)加權(quán)后的期刊受引用次數(shù)。引用次數(shù)的加權(quán)值由施引期刊的學(xué)科領(lǐng)域和聲望 (SJR) 決定。
SNIP:每篇文章中來(lái)源出版物的標(biāo)準(zhǔn)化影響將實(shí)際受引用情況對(duì)照期刊所屬學(xué)科領(lǐng)域中預(yù)期的受引用情況進(jìn)行衡量。
是否OA開(kāi)放訪問(wèn): | h-index: | 年文章數(shù): |
開(kāi)放 | 19 | 18 |
Gold OA文章占比: | 2021-2022最新影響因子(數(shù)據(jù)來(lái)源于搜索引擎): | 開(kāi)源占比(OA被引用占比): |
75.93% | 1.8 | 0.57... |
研究類文章占比:文章 ÷(文章 + 綜述) | 期刊收錄: | 中科院《國(guó)際期刊預(yù)警名單(試行)》名單: |
94.44% | SCIE | 否 |
歷年IF值(影響因子):
歷年引文指標(biāo)和發(fā)文量:
歷年中科院JCR大類分區(qū)數(shù)據(jù):
歷年自引數(shù)據(jù):
2023-2024國(guó)家/地區(qū)發(fā)文量統(tǒng)計(jì):
國(guó)家/地區(qū) | 數(shù)量 |
India | 27 |
CHINA MAINLAND | 23 |
USA | 16 |
England | 12 |
GERMANY (FED REP GER) | 12 |
Turkey | 11 |
Spain | 9 |
France | 8 |
Italy | 8 |
Portugal | 8 |
2023-2024機(jī)構(gòu)發(fā)文量統(tǒng)計(jì):
機(jī)構(gòu) | 數(shù)量 |
INDIAN INSTITUTE OF TECHNOLOGY S... | 13 |
HOCHSCHULE DARMSTADT | 7 |
INSTITUTO DE TELECOMUNICACOES | 7 |
SALZBURG UNIVERSITY | 5 |
UNIVERSIDADE DE LISBOA | 5 |
NORWEGIAN UNIVERSITY OF SCIENCE ... | 4 |
CHINESE ACADEMY OF SCIENCES | 3 |
ISTANBUL TECHNICAL UNIVERSITY | 3 |
NATIONAL INSTITUTE OF TECHNOLOGY... | 3 |
NORTHWESTERN POLYTECHNICAL UNIVE... | 3 |
近年引用統(tǒng)計(jì):
期刊名稱 | 數(shù)量 |
PATTERN RECOGN | 106 |
IEEE T PATTERN ANAL | 99 |
IEEE T INF FOREN SEC | 79 |
IEEE T IMAGE PROCESS | 62 |
IET BIOMETRICS | 53 |
PATTERN RECOGN LETT | 49 |
NEUROCOMPUTING | 39 |
EXPERT SYST APPL | 26 |
IMAGE VISION COMPUT | 22 |
IEEE T CIRC SYST VID | 19 |
近年被引用統(tǒng)計(jì):
期刊名稱 | 數(shù)量 |
IET BIOMETRICS | 53 |
IEEE ACCESS | 45 |
MULTIMED TOOLS APPL | 27 |
SENSORS-BASEL | 24 |
ACM COMPUT SURV | 19 |
IEEE T INF FOREN SEC | 17 |
PATTERN RECOGN LETT | 15 |
EXPERT SYST APPL | 12 |
APPL SCI-BASEL | 11 |
NEUROCOMPUTING | 11 |
近年文章引用統(tǒng)計(jì):
文章名稱 | 數(shù)量 |
Strengths and weaknesses of deep... | 24 |
Robust gait recognition: a compr... | 15 |
Employing fusion of learned and ... | 11 |
Grey Wolf optimisation-based fea... | 10 |
Unconstrained ear recognition us... | 10 |
Secure multimodal biometric syst... | 9 |
Hybrid robust iris recognition a... | 9 |
Domain adaptation for ear recogn... | 7 |
Ear verification under uncontrol... | 7 |
ScoreNet: deep cascade score lev... | 6 |
同小類學(xué)科的其他優(yōu)質(zhì)期刊 | 影響因子 | 中科院分區(qū) |
Journal Of Field Robotics | 4.2 | 2區(qū) |
Computer Science Review | 13.3 | 1區(qū) |
Computer Networks | 4.4 | 2區(qū) |
Journal Of Computational Science | 3.1 | 3區(qū) |
Ict Express | 4.1 | 3區(qū) |
Computer Speech And Language | 3.1 | 3區(qū) |
Applied Artificial Intelligence | 2.9 | 4區(qū) |
Neurocomputing | 5.5 | 2區(qū) |
Iet Software | 1.5 | 4區(qū) |
International Journal Of Approximate Reasoning | 3.2 | 3區(qū) |
若用戶需要出版服務(wù),請(qǐng)聯(lián)系出版商:WILEY, 111 RIVER ST, HOBOKEN, USA, NJ, 07030-5774。