scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals RANCANG BANGUN APLIKASI NEW NORMAL COVID-19 DETEKSI PENGGUNAAN MASKER MENGGUNAKAN HAAR CASCADE CLASSIFIER

2021 ◽  
Vol 15 (2) ◽  
pp. 199-209
Author(s):  
Ade chandra Saputra ◽  
Ahmadi Ahmadi ◽  
Ariesta Lestari
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Programming Language ◽  
Surveillance System ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Public Places ◽  
The Face ◽  
Haar Cascade ◽  
Analysis Design

During the COVID-19 pandemic, when in public places, it is required to apply the 4M health protocol, namely wearing masks, washing hands, maintaining distance, and avoiding crowds. In its implementation, there are officers who always maintain and remind people not to violate health protocols. Like remembering to wear a mask. The mask detection application is made as a computerized surveillance system that can store images of violations of the use of masks and provide warning sounds. Observations, discussions and literature studies are sources of data in this empirical research. Using Python as a programming language assisted with OpenCV for image processing. After passing through the 4 stages of Waterfall, namely Analysis, Design, Manufacturing and Development and Testing, an application is produced where the Raspberry Pi is a processing tool and images are captured from the camera module with a resolution of 1080x1024 px. This application can detect the use of masks with an accuracy of 90.5% using the Machine Learning Haar Cascade Classifier method. Where the condition of the face is a maximum of 30 degrees turned to the side and looked up

scholarly journals Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

scholarly journals Smart Door Lock/Unlock Using Raspberry Pi

2020 ◽  
pp. 543-548
Author(s):  
Sandesh R ◽  
Avinash Sridhar ◽  
Rishikesh T P ◽  
Saniya Farheen ◽  
Sara Tameem
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Images ◽  
Face Features ◽  
Image Capturing ◽  
The Face ◽  
Face Detection And Recognition ◽  
Door Lock ◽  
Human Faces

This paper deals with the proposed system for smart and savvy door lock recognition system which is essentially for identification of human faces and mainly for home security. This is divided into two sub systems. First is image capturing, then comes face detection and recognition and finally automatic door access management. Open CV is mainly used for Face Recognition because it uses Eigen faces which compares the face images and produces it without losing vital face features, facial images of various persons are going to be stored in database. The purpose of the paper is to take face recognition to height which can replace the use of standard passwords, pins and patterns, adding more security to our life. The process carried out by raspberry pi is fast and makes the system work smoother.

scholarly journals Haar Cascade dan Algoritma Eignface Untuk Sistem Pembuka Pintu Otomatis

2021 ◽  
Vol 4 (2) ◽  
pp. 182-192
Author(s):  
Indah Purwitasari Ihsan
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Prototype System ◽  
Image Processing Algorithm ◽  
Door Opening ◽  
Face Images ◽  
The Face ◽  
Haar Cascade ◽  
Access Rights ◽  
Shooting Distance

Teknologi diciptakan untuk mempermudah manusia dalam melakukan segala pekerjaan dan aktifitasnya, termasuk dalam hal mengakses pintu. Menggunakan teknologi pengolahan citra, wajah merupakan salah satu alternatif yang bisa digunakan untuk mengakses pintu dan mengamankannya dari orang yang tidak bertanggung jawab. Hal ini dikarenakan wajah setiap manusia memiliki pola yang berbeda-beda yang bisa ditransformasikan menjadi citra digital dan diolah mengunakan algoritma pengolahan citra. Dalam penelitian ini, mengkombinasikan haar cascade dan algoritma eigenface untuk mengolah citra wajah. Hasil dari pengolahan citra tersebut digunakan untuk menentukan hak akses dalam mengakses pintu, untuk kemudian diintegrasikan ke mikrokontroller, sehingga pintu dapat terbuka otomatis. Penelitian ini menghasilkan prototype system pembuka pintu otomatis dengan pengenalan wajah sebagai penentu hak aksesnya. Dari hasil penelitian, algoritma eigenface tidak dapat bekerja pada pencahayaan 0 lux  hingga 8 lux dalam jarak 20 cm hinga 60 cm  yaitu menghasilkan akurasi 0%, sedangkan pada pencahayaan 36 lux sampai 44 lux dan 160 lux sampai172 lux algoritma eigenface bekerja dengan baik dengan jarak pengambilan gambar 20-60 cm dengan akurasi 80%. Technology was created to make it easier for humans to do all their work and activities, including accessing doors. Using image processing technology, faces are an alternative that can be used to access doors and secure them from irresponsible people. This is because the face of every human being has a different pattern that can be transformed into a digital image and processed using an image processing algorithm. In this research, combining haar cascade and eigenface algorithm to processing face images. The results of the image processing are used to determine access rights in accessing the door, and then integrated into the microcontroller, so that the door can be opened automatically. This research produces a prototype automatic door opening system with face recognition as a determinant of access rights. From the results of the study, the eigenface algorithm cannot work at 0 lux  to 8 lux lighting within a distance of 20 cm  to 60 cm which produces 0% accuracy, while at 36 lux to 44 lux and 160 lux to 172 lux lighting the eigenface algorithm works well with a shooting distance of 20 cm to 60 cm with 80% accuracy.

scholarly journals Intelligent and Secured Bag using Artificial Intelligence

2020 ◽  
Vol 9 (1) ◽  
pp. 2237-2240
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Aged People ◽  
The Face ◽  
Haar Cascade ◽  
Almost All ◽  
Application Specific ◽  
Security Check

The Intelligent and Secured Bag is an application-specific design that can be useful for the security of important documents and valuable materials. The bag can carry out various features for daily use such as security check using face recognition. The system uses Artificial Intelligence for more effective results in terms of security in comparison with the existing system which uses fingerprint scanner. The Secured Bag consists of the facility of face recognition for advance security solution. The face recognition with Haar Cascade Classifier which is a machine learning object detection algorithm is used for the locking and unlocking of the bag which contributes in the intelligent part of the project. In order to reduce the forgetfulness of senior citizens and even professionals to pack the required items, RF-ID Technology will be used. It maintains the list of objects present in the bag. The RF-ID tags are attached to all the objects which is to be placed inside the bag. The RF-ID reader is used to read the tags which enters the bag. When any object will be missing from the bag, the message of the list of objects missing is send to the users mobile. For the security of the bag from thefts, magnetic lock is introduced. When the face of the person accessing the bag is not matched with the already existing database indicating that an unauthorized person is trying to open the bag, the lock will remain in the locked position. Thus, the person cannot access the bag. When the face of the person accessing the bag matches with the already existing database indicating that an authorized person is trying to open the bag, the lock will be unlocked and the person can access the bag. All the alert messages and the message of the list of items present and missing from the bag is sent to the owner using a GSM modem. The main advantage of using the Smart Bag is protection from thefts, also the owner of the bag gets informed about the theft and the items missing from the bag through GSM. Raspberry Pi will control all the distinguishable features. The smart bag can be used by almost all people including students, doctors, military people, aged people, etc. In general, it can be used in the daily life without the fear of something being stolen or missing from the bag.

scholarly journals Implementation of IoT in Workplace Monitoring and Safety Systems

2021 ◽  
Author(s):  
C. Annadurai ◽  
I. Nelson ◽  
X.N. Ranald Nivethan ◽  
Suraj Vinod ◽  
M. Senthil Kumar
Keyword(s):  
Air Pollution ◽  
Face Recognition ◽  
Rapid Development ◽  
Raspberry Pi ◽  
Intruder Detection ◽  
Workplace Monitoring ◽  
The Face ◽  
Haar Cascade ◽  
Safety Systems ◽  
E Mail

The continuous and rapid development in facilities in the workplace eventually calls for safety of the workplace premises as well as improved monitoring system. For instance, an intruder alert will be sent even if a client enters the premises. To eradicate this issue, an alert notification has to be sent only when required i.e., during an intruder detection or mishap detection. The data is collected by Raspberry Pi using the sensors interfaced to it. By employing the usage of IoT, data received from the sensors are sent to an IoT platform from where the information is passed as a notification through an email. The detected face from the video recorded by PiCam is sent to a local server using socket programming and the Face recognition is performed in the local server using Haar cascade and LBPH algorithm in Open CV. In case of an intruder detection, an e-mail notification is sent to the user. Similarly, when an accident or disaster is detected such as a fire accident or air pollution, an alert notification is sent to the user through an e-mail.

scholarly journals Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

2020 ◽  
Author(s):  
Kusworo ADI ◽  
Catur Edi WIDODO ◽  
Aris Puji WIDODO ◽  
Hilda Nurul ARISTIA
Keyword(s):  
Image Processing ◽  
Processing Technique ◽  
High Accuracy ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
The Face ◽  
Haar Cascade ◽  
Two Parameters ◽  
Significant Factors

Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

scholarly journals Prevention of unauthorized door access using face recognition built with Haar Cascade Classifier and Histogram of Oriented Gradients

2020 ◽  
Author(s):  
Arun Kumar Nadikattu ◽  
Kundhan P ◽  
John Shahid Sk ◽  
Sunita Panda ◽  
Kamalanathan Chandran
Keyword(s):  
Face Recognition ◽  
Remote Monitoring ◽  
Cascade Classifier ◽  
Security Systems ◽  
Web Based ◽  
Traditional System ◽  
Novel Approach ◽  
Haar Cascade ◽  
Door Lock ◽  
Physically Challenged

With the emergence of Internet of Things (IoT) along with its development of advanced authentication, both security and remote monitoring have become imperative as well as essential, and the need for smarter security systems has only been growing. The traditional system needs an individual to use a key or an identification (ID) card or a password to access the security doors. However, they have many limitations such as keys can be forged, recreation of ID cards and passwords can be stolen. To overcome, the existing system issues, a novel approach is proposed with the design and development of face authenticated web-based smart door lock control system using facial recognition and remotely monitoring the door. In this proposed system OpenCVs self-trained Haar Cascade Classifier along with Histogram of Gradient is used for face Recognition. Door will be unlocked when users face is recognised else will remain closed. In case an unauthorised person is found, the time of intrusion and the intruders image will be captured and sent to a separate server on discord, so that the user or the admin can view them at their convenience. The main usage of this system is to assist users for improvement of the door security of sensitive locations by using face recognition and is also designed by considering the physically challenged persons also.

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