Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada.
Department of Computer Science & Engineering, Indian Institute of Technology, Roorkee 247667, India.
Sensors (Basel). 2019 Oct 28;19(21):4641. doi: 10.3390/s19214641.
Many biometric systems based on physiological traits such as ones facial characteristics, iris, and fingerprint have been developed for authentication purposes. Such security systems, however, commonly suffer from impersonation attacks such as obfuscation, abrasion, latent samples, and covert attack. More conventional behavioral methods, such as passwords and signatures, suffer from similar issues and can easily be spoofed. With growing levels of private data readily available across the internet, a more robust authentication system is needed for use in emerging technologies and mobile applications. In this paper, we present a novel multimodal biometric user authentication framework by combining the behavioral dynamic signature with the the physiological electroencephalograph (EEG) to restrict unauthorized access. EEG signals of 33 genuine users were collected while signing on their mobile phones. The recorded sequences were modeled using a bidirectional long short-term memory neural network (BLSTM-NN) based sequential classifier to accomplish person identification and verification. An accuracy of 98.78% was obtained for identification using decision fusion of dynamic signatures and EEG signals. The robustness of the framework was also tested against 1650 impersonation attempts made by 25 forged users by imitating the dynamic signatures of genuine users. Verification performance was measured using detection error tradeoff (DET) curves and half total error rate (HTER) security matrices using true positive rate (TPR) and false acceptance rate (FAR), resulting in 3.75% FAR and 1.87% HTER with 100% TPR for forgery attempts.
许多基于生理特征（如面部特征、虹膜和指纹）的生物识别系统已经被开发出来用于身份验证。然而，这些安全系统通常容易受到伪装、磨损、潜在样本和隐蔽攻击等伪造攻击。更传统的行为方法，如密码和签名，也存在类似的问题，并且很容易被伪造。随着互联网上越来越多的私人数据的出现，需要一种更强大的身份验证系统来用于新兴技术和移动应用程序。在本文中，我们提出了一种新颖的多模态生物识别用户认证框架，将行为动态签名与生理脑电图（EEG）相结合，以限制未经授权的访问。我们在手机上收集了 33 位真实用户的 EEG 信号。使用基于双向长短期记忆神经网络（BLSTM-NN）的顺序分类器对记录的序列进行建模，以完成人员识别和验证。使用动态签名和 EEG 信号的决策融合对识别获得了 98.78%的准确率。该框架的稳健性还通过模仿真实用户的动态签名，对 25 位伪造用户的 1650 次伪造尝试进行了测试。使用检测误差权衡（DET）曲线和半总错误率（HTER）安全矩阵，通过真阳性率（TPR）和假阳性率（FAR）来测量验证性能，对于伪造尝试，FAR 为 3.75%，HTER 为 1.87%，TPR 为 100%。
