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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.13617 |
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| _version_ | 1866916908052250624 |
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| author | Ayop, Zakiah Rosdi, Wan Mohamad Hariz Bin Wan Mohamad Hua, Looi Wei Anawar, Syarulnaziah Othman, Nur Fadzilah |
| author_facet | Ayop, Zakiah Rosdi, Wan Mohamad Hariz Bin Wan Mohamad Hua, Looi Wei Anawar, Syarulnaziah Othman, Nur Fadzilah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm Ayop, Zakiah Rosdi, Wan Mohamad Hariz Bin Wan Mohamad Hua, Looi Wei Anawar, Syarulnaziah Othman, Nur Fadzilah Computer Vision and Pattern Recognition 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. |
| title | Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13617 |