Door lock system via web application

Vongchumyen, Charoen; Watanachaturaporn, Pakorn; Jinjakam, Chompoonuch; Watcharapupong, Akkradach; Kasemsiri, Watjanapong; Tongprasert, Kiatnarong; Walairacht, Aranya; Penpokai, Thaweesak; Jenweerawat, Thongchai; Hami, Aunnisa

doi:10.1109/ieecon.2017.8075909

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…This system performs better for both known and unknown images. The authors in [ 15 ] have proposed door lock unlock system using web application. In this work, they monitor the door for locking and unlocking using web application.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Hussain

et al. 2022

Computational and Mathematical Methods in Medicine

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Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.

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Section: Related Workmentioning

confidence: 99%

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Hussain

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Charoen Vongchumyen et.al. [24] proposes the smart way to solve problem in conventional door-locking system. The system can remotely lock an un-lock the door, check the door state, notify the owner whether there is someone knocking the door, and generate temporary pass code that can be use in specific date and time.…”

Section: Results and Analysismentioning

confidence: 99%

Integrated smart door system in apartment room based on internet

et al. 2019

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Internet of Things (IoT) is technology which provides communication between machines without human intervention.There are many applications that implement IoT technology e.g. smart power, smart home, transportation management, and smart city. This paper proposes the usage of IoT to develop electronic key that can help to improve the security in apartment. Electronic key uses Android smart phone to access and control the door. Electronic key uses Raspberry pii, motor DC, webcam, microswitch, and Arduino GSM. Raspberry is used to get command from server and instruct the Arduino GSM to open or close the door and webcam is used to take picture from the environment for documentation. This research uses three steps to verify the user who want to open or close the door i.e. using username and password, bluetooth, and QR code scanner.

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“…Teknologi saat ini berkembang dengan sangat pesat dan merupakan salah satu bidang yang mempunyai peran penting dalam beberapa aspek kehidupan manusia, termasuk pada bidang security. Saat ini sudah banyak berkembang sebuah sistem pengamananan akses masuk pada sebuah rumah atau ruangan dengan sistem komputer dengan beberapa verifikasi identitas, baik dengan menggunakan kartu [1], password [2] [3], smartphone [4] dan sebagainya. Salah satu manfaat dari pengembangan teknologi ini adalah untuk meminimalisir terjadinya tindak kejahatan.…”

Section: Pendahuluanunclassified

Sistem Akses Pintu Berbasis Face Recognition Menggunakan ESP32 Module dan Aplikasi Telegram

Nuraeni

Anggraini

et al. 2021

JMTIK

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Kasus pencurian yang terjadi terkadang didukung oleh kelalaian pengguna dalam mengontrol kunci pintu. Beberapa penelitianterkait sistem penguncian pintu berbasis teknologi informasi dan komunikasi telah dikembangkan untuk mengatasi permasalahan tersebutnamun membutuhkan biaya yang besar untuk penerapannya. Pada makalah ini telah dibangun sistem penguncian pintu cerdas berbasispengenalan wajah yang dapat diterapkan dengan biaya yang terjangkau karena menggunakan aplikasi gratis yaitu Telegram untuk memantaudan mengontrol sistem penguncian. Hasil pengujian yang telah diperoleh adalah rata-rata waktu pengiriman data dari ESP32 ke Telegramselama 4,74 detik dan rata-rata waktu pengontrolan dari Telegram ke Relay adalah 0,81 detik. Sistem kunci pintu yang dibangun dapatmembantu pengguna untuk memantau dan mengontrol kunci dari jarak jauh dan dengan biaya implementasi yang rendah sehingga kelalaianpengguna dapat diminimalisir.

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Door lock system via web application

Cited by 11 publications

References 2 publications

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Integrated smart door system in apartment room based on internet

Sistem Akses Pintu Berbasis Face Recognition Menggunakan ESP32 Module dan Aplikasi Telegram

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