Gorur Kutlucan
Electrical and Electronics Engineering Department, Bandırma Onyedi Eylul University, 10250 Balıkesir, Turkey.
Biomimetics (Basel). 2023 Aug 18;8(4):378. doi: 10.3390/biomimetics8040378.
In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication ( verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students.
近年来，当前文献中基于眼电图（EOG）的生物特征认证系统的相关研究较少，这些研究主要涉及眼动或眨眼活动。眼电图具有永久性和独特性，可用于区分个体。在这项工作中，我们研究了基于FSST（傅里叶同步挤压变换）-ICA（独立成分分析）-EMD（经验模态分解）鲁棒框架的EOG生物特征认证（验证）性能，该性能使用集成的RNN（递归神经网络）深度模型来处理自愿眨眼动作。实施FSST是为了在最先进的时频矩阵上提供眼电图准确且密集的时空特性。ICA是一种强大的统计工具，用于分解多个记录电极。最后，部署EMD以从头皮采集的脑电图中分离出眼电图信号。据我们所知，这是在有限的与眼电图相关的生物特征文献中，首次探索FSST-ICA-EMD框架在通过自愿眨眼活动生成的EOG生物特征认证方面的成功尝试。根据这些有前景的结果，除了在七个人中获得高TAR（真接受率）分数（≥98%）和低FAR（误接受率）分数（≤3.33%）之外，还实现了更高的识别准确率（ACC/准确率：≥99.99%，AUC/曲线下面积：0.99）。另一方面，由于数字世界（电子学习、电子银行或电子政务系统）的增加以及新冠疫情，对在线用户/学生的认证和监控正变得至关重要。特别是为了确保可靠访问，在电子学习平台和在线教育策略中，需要一种高度可扩展且经济实惠的方法来对考生进行认证，同时防止作弊或监控高数据量的视频流。因此，这项工作可能会提出一种方法，为包括用户/学生的电子学习平台在内的数字应用提供可持续、连续且可靠的EOG生物特征认证。
