Beyond Speech: Generalizing D-Vectors for Biometric Verification
Beyond Speech: Generalizing D-Vectors for Biometric Verification
复制标题
超越言语：推广用于生物识别验证的 D 向量
DOI：
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Robert Wright
中科院分区：
文献类型：
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作者：
Jacob Baldwin;Ryan Burnham;Andrew Meyer;Robert Dora;Robert Wright
基于深度学习的自动特征提取方法从根本上改变了说话人识别和面部识别。目前的方法通常专用于各个领域，例如用于说话人识别的深度向量（D-Vectors）。我们提供了两个不同的贡献：一个广义的框架，生物识别验证的启发D-向量和新的模型，优于目前最先进的方法。我们的方法支持各种特征提取模型的替代，并提高了跨域验证测试的鲁棒性。我们展示了两个不同的行为生物特征验证问题的框架和模型：步态和移动的步态。我们提出了一个全面的实证分析比较我们的框架，在这两个领域的最先进的。我们的模型使用数量级更少的数据比这两个领域中最先进的方法进行更高精度的验证。我们相信，高准确性和实际数据要求的结合将使行为生物识别模型能够在实验室之外应用，以支持急需的网络安全改进。
Deep learning based automatic feature extraction methods have radically transformed speaker identification and facial recognition. Current approaches are typically specialized for individual domains, such as Deep Vectors (D-Vectors) for speaker identification. We provide two distinct contributions: a generalized framework for biometric verification inspired by D-Vectors and novel models that outperform current stateof-the-art approaches. Our approach supports substitution of various feature extraction models and improves the robustness of verification tests across domains. We demonstrate the framework and models for two different behavioral biometric verification problems: keystroke and mobile gait. We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains. Our models perform verification with higher accuracy using orders of magnitude less data than state-of-the-art approaches in both domains. We believe that the combination of high accuracy and practical data requirements will enable application of behavioral biometric models outside of the laboratory in support of much-needed improvements to cyber security.
通过融合打字、滑动和手机移动模式对智能手机用户进行持续身份验证
DOI：
10.1109/btas.2016.7791164
发表时间：
2016
期刊：
2016 IEEE 8th International Conference on
影响因子：
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作者：
Kumar, Rajesh;Phoha, Vir V.;Serwadda, Abdul
通讯作者：
Serwadda, Abdul