用改进LBPH算法实现戴口罩人脸识别的双因子门禁系统
Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm
- 结合人脸识别与密码验证，改进LBPH算法处理口罩遮挡
- 平均准确率70%，召回率达83.26%，可识别陌生人并报警
- 基于树莓派+Telegram远程控制，适合智能门禁场景
疫情期间，口罩检测在智能门禁中愈发重要。现有基于物联网的面部检测模型多聚焦于无遮挡场景，缺乏对口罩遮挡的适配。本文提出一种双因子认证系统，通过人脸识别与密码验证实现智能门禁控制，并在检测到陌生人时自动通知用户、启动监控，支持通过Telegram远程管理。系统采用局部二值模式直方图（LBPH）进行全脸识别，改进版LBPH用于遮挡面部检测。测试结果显示，系统平均准确率约70%，精确率约80%，召回率约83.26%。实测表明系统可有效完成人脸与口罩检测，自动化注册用户、开关门及通知功能，用户接受度高。
原文摘要 · Abstract (English)
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.
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