Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm

TL;DR

This paper presents a two-factor smart entry system combining facial recognition with a modified LBPH algorithm for mask detection, enabling remote control and owner notification with promising accuracy and user acceptance.

Contribution

It introduces a novel IoT-based face mask detection and recognition system using a modified LBPH algorithm for occluded faces, integrated with passcode verification and remote control features.

Findings

Achieved approximately 70% accuracy in face recognition.

System successfully detects masks and recognizes faces in real-time.

High user acceptance for the proposed system.

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.