Home Security System with Face Recognition based on Convolutional Neural Network

Abstract

Security of house doors is very important and becomes the basis for the simplest and easiest security and sufficient to provide a sense of security to homeowners and along with technological developments, especially in the IoT field, which makes technological developments in locking house doors have developed a lot like locking house doors with faces and others. The development of facial recognition systems has also developed and has been implemented for home door locking systems and is an option that is quite simple and easy to use and is quite accurate in recognizing the face of homeowners. The development of the CNN method in facial recognition has become one of the face recognition systems that are easy to implement and have good accuracy in recognizing faces and has been used in object recognition systems and others. In this study, using the CNN Alexnet facial recognition system which is implemented in a door locking system, data collection is done by collecting 1048 facial data on the face of the homeowner using a system which is then used to train machine learning where the results are quite accurate where the accuracy is the result is 97.5% which is quite good compared to some other studies. The conclusion is the CNN Alexnet method can perform facial recognition which is quite accurate which can be implemented on the IoT device, namely, the Raspberry Pi.

SAFE: Security Door Lock System Using Haar-Cascade and LBPH Method

“…The accuracy of the system is high but result may be affected by the illumination and dark region. [26] employed CNN to construct a face recognition-based door locking system and positioned the raspberry pi next to the door. An external computer was used to train the model as the raspberry pi had less computational power.…”

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

“…It is important to notice that the second most largest category goes to private datasets with 34 papers (Zhou et al, 2018;Ara et al, 2017;Phankokkruad, 2018;Gilani and Mian, 2018;Khan et al, 2019b;Qin et al, 2019;Liu et al, 2019;Peng et al, 2019;Mangal et al, 2020;Lv et al, 2020;Perti et al, 2020;Kim et al, 2017;Irjanto and Surantha, 2020;Arafah et al, 2020;Prasetyo et al, 2021;Moon et al, 2017;Chandran et al, 2018;Yang et al, 2018;Son et al, 2020;Alhanaee et al, 2021;Khan et al, 2020;Nakajima et al, 2021;Talahua et al, 2021;He and Ding, 2023;Karlupia et al, 2023;Bussey et al, 2017;Li et al, 2022;Filippidou and Papakostas, 2020;Bussey et al, 2017;Singh et al, 2022;Setio Aji et al, 2022;Wang et al, 2022;Lestari et al, 2021) creating their own datasets for testing. There are advantages and disadvantages to developing and utilizing private face image datasets for CNN-based face recognition.…”

Face Recognition System Design and Implementation using Neural Networks

“…The accuracies of the proposed system were 99.7% and 94.02% respectively. The authors of [24] proposed a home security system that uses face recognition technology developed by CNN. The Raspberry Pi was used as a microcontroller so that when the face of the homeowner was detected, the door will be unlocked automatically.…”