通过深度学习增强基于生物特征胶囊的身份验证和面部识别

Tyler Phillips, X. Zou, Feng Li, Ninghui Li
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引用次数: 14

摘要

近年来,开发人员利用智能设备中生物识别传感器的激增,以及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。尽管这些系统表现出卓越的性能,并得到了广泛的接受,但它们提出了独特而紧迫的安全和隐私问题。一种提出的解决这些问题的方法是优雅的,基于融合的生物胶囊方法。该方法在安全特征融合设计上具有可证明的安全性、保密性、可取消性和灵活性。在这项工作中,我们将bioccapsule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整合到基于bioccapsule的面部认证系统中,以进一步提高安全识别的准确性。我们将底层识别系统的性能与生物胶囊嵌入式系统的性能进行比较,以证明生物胶囊方案对底层系统性能的最小影响。我们还证明了生物胶囊方案优于或执行以及许多其他提出的安全生物识别技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning
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.
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