IET Biometrics
| 短名 | IET Biom. |
| Journal Impact | 1.75 |
| 国际分区 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q3) |
| 期刊索引 | SCI Q4中科院 4 区 |
| ISSN | 2047-4938, 2047-4946 |
| h-index | 41 |
| 国内分区 | 计算机科学(4区)计算机科学计算机人工智能(4区) |
生物特征识别领域——基于个人行为和生物特征的自动识别——现在已经达到了成熟的水平，可行的实际应用既可能而且越来越可用。生物识别领域的特点尤其是其跨学科性，因为虽然主要侧重于强大的技术基础，但有效的系统设计和实施通常需要广泛的技能，例如人为因素、数据安全和数据库技术、心理和生理意识， 等等。此外，技术重点本身包含多样性，因为有效的生物识别系统工程需要集成图像分析、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。期刊的范围有意相对广泛。在关注核心技术问题的同时，人们认识到这些问题可能本质上是多样化的，并且在许多情况下可能会跨越传统的学科界限。因此，该期刊的范围将包括任何可以证明一篇论文可以增加我们对生物识别系统的理解、预示未来生物识别技术的发展和应用、或促进相关技术的更多实际应用的主题：开发和增强个体生物识别模式包括已建立的和传统的模式（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、神经模式等）。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人为因素和生物识别系统的人机界面问题、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，心理和生理原理和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、安全工程影响、智能卡和实施中的相关技术、实施平台、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、安全性和生物识别相关加密与取证处理和跨学科共性的链接核心支撑技术（例如图像分析、模式识别、计算机视觉、信号处理等），其中可以证明与生物识别处理的具体相关性应用和应用主导的考虑关于技术或生物识别系统开发的工业背景的职位论文采用和促进生物识别标准，提高技术接受度、部署和互操作性，避免跨文化和跨部门限制相关的道德和社会问题
期刊主页投稿网址| 涉及主题 | 计算机科学人工智能计算机视觉心理学认知心理学模式识别（心理学）计算机安全数学生物识别哲学语言学生物机器学习几何学语音识别物理特征提取程序设计语言社会学医学统计 |
| 出版信息 | 出版商: John Wiley & Sons Inc.，出版周期: Bi-monthly，期刊类型: journal |
| 基本数据 | 创刊年份: 2012，原创研究文献占比: 97.37%，自引率:0.00%， Gold OA占比: 75.93% |
| 平均审稿周期 | 网友分享经验：33 Weeks |
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