Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.

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深钥匙

生物特征认证涉及各种技术，通过利用他们独特的、可测量的生理和行为特征来识别个人。然而，传统的生物特征认证系统（例如，人脸识别、虹膜、视网膜、语音和指纹）被生物特征工具（如防监视面具、隐形眼镜、声码器或指纹膜）欺骗的风险越来越大。在本文中，我们设计了一个名为 DeepKey 的多模式生物特征认证系统，该系统同时使用脑电图 (EEG) 和步态信号来更好地防范此类风险。DeepKey 由两个关键组件组成：一个用于阻止未经授权的受试者的无效 ID 过滤器模型，以及一个基于基于注意力的循环神经网络 (RNN) 的识别模型，用于并行识别受试者的 EEG ID 和步态 ID。只有在所有组件产生一致的确认以匹配用户声明的身份时，才能授予主题访问权限。我们通过在我们大学的现场部署来实施 DeepKey，并进行广泛的实证实验以研究其在实践中的技术可行性。DeepKey 分别实现了 0% 和 1.0% 的错误接受率 (FAR) 和错误拒绝率 (FRR)。初步结果表明，DeepKey 是可行的，与一组方法相比，表现出始终如一的优越性能，并有可能应用于现实环境中的身份验证部署。我们通过在我们大学的现场部署来实施 DeepKey，并进行广泛的实证实验以研究其在实践中的技术可行性。DeepKey 分别实现了 0% 和 1.0% 的错误接受率 (FAR) 和错误拒绝率 (FRR)。初步结果表明，DeepKey 是可行的，与一组方法相比，表现出始终如一的优越性能，并有可能应用于现实环境中的身份验证部署。我们通过在我们大学的现场部署来实施 DeepKey，并进行广泛的实证实验以研究其在实践中的技术可行性。DeepKey 分别实现了 0% 和 1.0% 的错误接受率 (FAR) 和错误拒绝率 (FRR)。初步结果表明，DeepKey 是可行的，与一组方法相比表现出始终如一的优越性能，并有可能应用于现实世界环境中的身份验证部署。