Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning
Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning
复制标题
通过深度学习增强基于生物识别胶囊的身份验证和面部识别
DOI：
10.1145/3322431.3325417
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Li, Ninghui
中科院分区：
文献类型：
--
作者：
Phillips, Tyler;Zou, Xukai;Li, Feng;Li, Ninghui
近年来，开发人员利用智能设备中生物识别传感器的激增，沿着深度学习的最新进展，实现了一系列基于生物识别的身份验证系统。虽然这些系统表现出卓越的性能并得到广泛的认可，但它们存在独特而紧迫的安全和隐私问题。一种解决这些问题的方法是优雅的基于融合的BioCapsule方法。BioCapsule方法在其安全特征融合设计中具有可证明安全性、隐私保护性、可撤销性和灵活性。在这项工作中，我们将BioCapsule扩展到基于人脸的识别。此外，我们将最先进的深度学习技术融入到基于BioCapsule的面部认证系统中，以进一步提高安全识别的准确性。我们比较了底层识别系统的性能的BioCapsule嵌入式系统的性能，以证明底层系统性能的BioCapsule计划的最小影响。我们还证明了BioCapsule计划优于或执行以及许多其他提出的安全生物识别技术。
In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques.
DOI：
10.1109/ciisp.2007.369304
发表时间：
2007-04
期刊：
2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing
影响因子：
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作者：
M. Dabbah;W. L. Woo;S. Dlay
通讯作者：
M. Dabbah;W. L. Woo;S. Dlay
在不受控制的环境中实时检测人脸
DOI：
--
发表时间：
1997
期刊：
Electronic imaging
影响因子：
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作者：
A. Nefian;M. Khosravi;M. Hayes
通讯作者：
M. Hayes