Two-factor authentication smart entryway using modified LBPH algorithm
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
AIRCC Publishing Corporation
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28496/2/00099271220241653121513.pdf http://eprints.utem.edu.my/id/eprint/28496/ https://aircconline.com/ijwmn/V16N6/16624ijwmn01.pdf |
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| Summary: | 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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