Jonathan Crystal Galle Oppier, Abdul Kholiq

IMPLEMENTASI SISTEM KEAMANAN RUMAH BERBASIS FACE RECOGNITION DENGAN PERINGATAN ALARM OTOMATIS MENGGUNAKAN METODE LOCAL BINARY PATTERNS HISTOGRAM

Introduction

Implementasi sistem keamanan rumah berbasis face recognition dengan peringatan alarm otomatis menggunakan metode local binary patterns histogram. Rancang bangun sistem keamanan rumah otomatis berbasis face recognition (LBPH) terintegrasi IoT dengan ESP32-CAM. Deteksi akurat 95.42%, respon cepat, & peringatan Telegram. Solusi efektif & ekonomis.

Abstract

This research aims to design and implement a home security system based on facial recognition that is capable of working in real-time and integrated with the Internet of Things (IoT), in order to overcome the limitations of conventional security systems that are not yet able to detect and respond to potential threats automatically and still rely on manual supervision. The system was developed using an ESP32-CAM module with a Local Binary Pattern Histogram (LBPH) algorithm for the facial identification process, and is integrated with a buzzer and the Telegram application as a two-layer warning system. The method used is prototyping with an iterative approach for two months through direct testing in a residential environment. The test results show that the system is able to recognize faces with 95.42% accuracy, provides a fast response, and works stably in various lighting conditions. The conclusion of this research shows that the system is effective, economical, and can be implemented without major changes to the building structure. This system also shows potential for further development on a more complex smart home scale. 

Review

The paper "IMPLEMENTASI SISTEM KEAMANAN RUMAH BERBASIS FACE RECOGNITION DENGAN PERINGATAN ALARM OTOMATIS MENGGUNAKAN METODE LOCAL BINARY PATTERNS HISTOGRAM" presents a highly relevant and timely contribution to the field of smart home security. The research effectively addresses the limitations of conventional security systems by proposing an automated, real-time facial recognition solution integrated with the Internet of Things. The objective of enhancing threat detection and response capabilities automatically, moving beyond manual supervision, is well-aligned with current technological advancements and user demands for more intelligent and proactive security measures. A significant strength of this work lies in its practical implementation using accessible and cost-effective technologies. The choice of the ESP32-CAM module combined with the Local Binary Pattern Histogram (LBPH) algorithm for facial identification demonstrates a pragmatic approach to developing a real-time system suitable for residential environments. The dual-layer warning system, utilizing both a physical buzzer and Telegram notifications, is a well-conceived feature that enhances reliability and ensures prompt user awareness. The reported test results are particularly encouraging, showcasing a high face recognition accuracy of 95.42%, fast response times, and stable performance across various lighting conditions, making a strong case for its effectiveness, economical nature, and ease of integration without significant structural modifications. While the abstract highlights a robust and promising system, the full paper would benefit from a more detailed exploration of certain aspects. A deeper comparative analysis of the LBPH algorithm against other state-of-the-art facial recognition techniques, specifically within the constraints of the ESP32-CAM's processing power, would further justify its selection. Clarification on the dataset size and diversity used for training and testing, particularly concerning different individuals and varying lighting scenarios, would strengthen the validity of the reported accuracy. Finally, while the abstract mentions potential for "further development on a more complex smart home scale," a discussion on specific challenges and strategies for integrating this system with other smart home components (e.g., smart locks, environmental sensors) and addressing privacy concerns inherent in facial recognition systems would provide a more holistic and forward-looking perspective.

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