Abstract
Face mask detection has become increasingly important recently, particularly during the COVID-19 pandemic. Many face detection models have been developed in smart entryways using IoT. However, there is a lack of IoT development on face mask detection. This paper proposes a two-factor authentication system for smart entryway access control using facial recognition and passcode verification and an automation process to alert the owner and activate the surveillance system when a stranger is detected and controls the system remotely via Telegram on a Raspberry Pi platform. The system employs the Local Binary Patterns Histograms for the full face recognition algorithm and modified LBPH algorithm for occluded face detection. On average, the system achieved an Accuracy of approximately 70%, a Precision of approximately 80%, and a Recall of approximately 83.26% across all tested users. The results indicate that the system is capable of conducting face recognition and mask detection, automating the operation of the remote control to register users, locking or unlocking the door, and notifying the owner. The sample participants highly accept it for future use in the user acceptance test.
Abstract (translated)
最近，面部口罩检测变得越来越重要，特别是在COVID-19大流行期间。许多智能入口处的面部识别模型已经使用物联网技术开发出来，然而，在面部口罩检测方面的物联网开发却相对不足。本文提出了一种用于智能入口门禁控制的双因素认证系统，该系统结合了面部识别和密码验证，并在检测到陌生人时通过自动化过程向所有者发出警报并激活监控系统，同时还可以通过Telegram平台远程控制树莓派上的整个系统运行。 该系统采用局部二值模式直方图（LBPH）算法进行全脸识别，并且使用修改后的局部二值模式算法来检测被遮挡的面部。经过测试，在所有用户中，平均准确率约为70%，精确度约为80%，召回率为约83.26%。 结果显示，该系统能够执行面部识别和口罩检测、远程控制操作自动化（包括注册用户、锁定或解锁门以及通知所有者）。在用户体验接受性测试中，参与者对该系统的未来使用表示高度认可。
URL
https://arxiv.org/abs/2508.13617
