Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm

📅 2025-08-19
📈 Citations: 0
Influential: 0
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🤖 AI Summary
To address the lack of robust two-factor authentication and IoT interoperability in smart access control systems under pandemic-related mask-wearing conditions, this work designs and implements a lightweight edge-based access control system built on Raspberry Pi. We propose an enhanced Local Binary Patterns Histograms (LBPH) algorithm to improve facial feature extraction under partial occlusion, and integrate face-plus-PIN two-factor authentication with Telegram Bot API for remote alerting, real-time surveillance coordination, and user registration management. All recognition and response operations are executed locally at the edge, achieving an average accuracy of 70%, precision of 80%, and recall of 83.26%. The system demonstrates high user acceptance and practical deployability. Key contributions include: (1) an occlusion-aware LBPH optimization tailored for masked-face recognition; and (2) a unified edge architecture that synergistically combines bi-modal authentication with IoT-enabled remote management.

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📝 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.
Problem

Research questions and friction points this paper is trying to address.

Develops IoT-based face mask detection for smart entryways
Proposes two-factor authentication using facial recognition and passcodes
Implements modified LBPH algorithm for occluded face detection
Innovation

Methods, ideas, or system contributions that make the work stand out.

Modified LBPH algorithm for occluded faces
Two-factor authentication with facial recognition
Raspberry Pi IoT platform with Telegram integration
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