生物识别安全的挑战与机遇:调查

Shefali Arora, M. Bhatia
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引用次数: 8

摘要

生物特征识别系统根据人脸、指纹、虹膜等独特特征来识别个体。本研究的主要目的是了解深度学习在身份验证过程中的作用,以及它在增强生物识别系统安全性方面的应用。我们重点介绍了在理想和非理想环境条件下使用深度学习方法验证注册用户的研究。我们总结了这些方法,并探讨了继续限制生物识别系统充分发挥潜力的挑战。最重要的是:为身份验证构建健壮的算法,确保注册模板的安全性,并保护系统免受欺骗攻击。在本文中,我们回顾了各种研究在克服上述挑战方面取得的成绩,以及该领域的潜在改进和未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and opportunities in biometric security: A survey
ABSTRACT Biometric systems identify individuals based on unique traits such as the face, fingerprints, iris etc. The main objective of the study is to understand the role of deep learning in the process of authentication as well as its application in the enhancement of security of biometric systems. We highlight the studies using deep learning approaches to authenticate enrolled users under ideal and non-ideal environmental conditions. We summarize these approaches and explore the challenges that continue to restrict the full potential of biometric systems. The foremost are: building robust algorithms for authentication, ensuring the security of enrolled templates and protecting systems against spoofing attacks. In this paper, we review the performance achieved by various studies in overcoming the aforesaid challenges, along with the potential improvements and future directions in this domain.
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