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Journal : Proceeding Applied Business and Engineering Conference

Automatic Door Lock Based on Knock Pattern and Face Detection Zulhaq, Danang Fariz; Fahruzi, Iman
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Residential burglaries are prevalent offenses, frequently occurring during the absence of homeowners. These offensesgenerally entail breaching doors or windows. An innovative home security system is important to resolve this issue. This researchseeks to create an automated door locking mechanism utilizing knock patterns and facial recognition to improve residential security.The system incorporates a piezoelectric sensor for detecting knock patterns and an ESP32-Cam for facial recognition. The studymethodology entails the development and evaluation of a system that integrates two primary components, guaranteeing that thedoor unlocks solely upon the recognition of both an accurate knock pattern and a registered facial image. The system's accuracywas assessed under varying lighting situations and distances to determine its efficacy. The results indicate that the face detectionsystem operates effectively under optimal lighting settings, and the knock pattern system activates the door lock mechanism whenthe knock intervals correspond to the pre-registered pattern. Nonetheless, the system encounters difficulties in recognizing faces inlow-light conditions. The door lock is secure, as it will only unlock when both the appropriate facial recognition and knock patterncriteria are satisfied, thus improving security relative to conventional locks. This dual-layer security strategy mitigates the dangersinherent in traditional systems, such as keys or PINs, which are susceptible to theft or circumvention. The proposed technologysignificantly enhances home security, presenting a more secure and user-friendly alternative to current options. Futureenhancements may concentrate on augmenting the precision of facial detection in low-light environments and refining the systemfor wider practical applications.
Smart Access Control System Based On Uncontrolled Environment Human Face Recognition Using Convolutional Neural Network Akbar, Muhammad Ikram Andrianur; Rinaldi, Anggi; Fahruzi, Iman
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Neural networks or other artificial intelligence methods have developed rapidly over the past four decades. Itsuse in various fields makes many people compete to develop it further. One of the applications of artificial intelligence isan automatic door opening and closing system. This development can provide many advantages for users, one of which isthat there is no need to make direct contact with the door handle. Armed with a capable PC and an esp32 microcontroller,the system works by detecting images of user facial expressions approaching the object using a webcam. If the requiredinput matches the system rules, the motor will move to open the door. By using convolutional neural network technique,the system can classify the image quickly. Several expressions such as angry, disgusted, scared, happy, sad, surprised, andnormal can be the door-opening key of the system. The user can select one to use as the input key to drive the motor toopen the door. The study outcomes for several predetermined facial expressions yielded an accuracy rate of 60% and adetection time of under 4 seconds. The detectable distance extends to ± 2 meters. Further study could enable thedevelopment of this autonomous door with an IoT-based system for enhanced efficiency. Hopefully, this research caninfluence the development of intelligent building systems and other fields of artificial intelligence technology.