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科技导报
| 综述 2021, 39(19): 74-82
大数据生物特征识别技术研究进展
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刘琦1 , 于汉超2 , 蔡剑成3 , 韩琥3
作者信息
1. 河南警察学院, 郑州 450000;
通讯作者:
于汉超(通信作者),博士,研究方向为信息科学及科研管理,电子信箱:hcyu@cashq.ac.cn
Research progress and trend of big-data based biometrics
Affiliations
出版时间: 2021-10-13
doi: 10.3981/j.issn.1000-7857.2021.19.009
文章导航
随着机器学习领域研究的持续发展，特别是深度学习方面的进步及图像处理器（GPU）等算力的持续提高，利用生物特征大数据的识别技术获得广泛关注，并在人证比对、智能监控以及疫情防控等多个领域取得了很好的应用。分析了大数据生物特征识别技术的发展态势，总结了生物特征类型以及大数据驱动的生物特征识别技术发展与应用，探讨了大数据生物特征识别技术的未来发展趋势。
生物特征识别
/
大数据驱动
/
自监督学习
/
领域通用特征
/
人工智能
Biometric recognition refers to the use of inherent physiological or behavioral characteristics of human body for personal identification, such as face recognition, fingerprint recognition, and iris recognition. Early technologies are usually based on handcrafted features and traditional machine learning methods, and constrained by data and computing power acquisition ability. Thus, they are generally limited to controlled environment and difficult to build powerful biometric recognition models by making use of large-scale biometric data effectively. In recent years, with the advancement of machine learning, especially deep learning and increasing computing power of GPU, big data based biometric recognition technologies have received widespread attention and have been widely used in the areas such as personal authentication, intelligent video surveillance, and prevention and control of epidemic. This paper summarizes the development of big-data based biometrics technologies, covering different types of biometrics and their applications, and then discusses the trend of big-data based biometrics technologies in the future.
biometric recognition
/
big data driven
/
self-supervised learning
/
universal domain representation
/
artificial intelligence
刘琦, 于汉超, 蔡剑成, 韩琥.
大数据生物特征识别技术研究进展.
科技导报,
2021
, 39
(19)
: 74
-82
.
DOI: 10.3981/j.issn.1000-7857.2021.19.009
LIU Qi, YU Hanchao, CAI Jiancheng, HAN Hu.
Research progress and trend of big-data based biometrics[J].
Science & Technology Review ,
2021
, 39
(19)
: 74
-82
.
DOI: 10.3981/j.issn.1000-7857.2021.19.009
2021年第39卷第19期
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文章信息
doi: 10.3981/j.issn.1000-7857.2021.19.009
接收时间：2020-06-23
首发时间：2021-11-08
出版时间：2021-10-13
收稿日期：2020-06-23
修回日期：2021-09-07
通讯作者:
于汉超(通信作者),博士,研究方向为信息科学及科研管理,电子信箱:hcyu@cashq.ac.cn
https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2021.19.009
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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