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 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.

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.

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 Real Time Face Recognition based Smart Lab for Energy Conservation

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 32-41
Author(s):  
M. Karthikeyan ◽  
T.S. Subashini ◽  
M.S. Prashanth
Keyword(s):  
Face Recognition ◽  
Object Identification ◽  
Home Automation ◽  
Human Beings ◽  
Environmentally Conscious ◽  
Repetitive Tasks ◽  
Automation Systems ◽  
The Face ◽  
Identification Technique ◽  
Haar Cascade

Home automation offers a good solution to help conserve our natural resources in a time when we are all becoming more environmentally conscious. Home automation systems can reduce power consumption and when they are not in use automatically turn off lights and appliances. With home automation, many repetitive tasks can be performed automatically or with fewer steps. For example, each time the person gets out of his computer desk, for instance, the fan and the lights need to be turned off and switched on when he comes back to the computer desk. This is a repetitive task, and failure to do so leads to a waste of energy. This paper proposes a security/energy saving system based on face recognition to monitor the fan and lights depending on the presence or absence of the authenticated user. Initially, the authenticated faces/users LBPH (Local Binary Pattern Histogram) features were extracted and modelled using SVM to construct the face profile of all authenticated users. The webcam catches the user's picture before the PC and the Haar-cascade classifier, a profound learning object identification technique is used to identify face objects from the background. The facial recognition techniques were implemented with python and linked to the cloud environment of Ada-Fruit in order to enable or disable the light and fan on the desk. The relay status is transmitted from Ada Fruit Cloud to Arduino Esp8266 using the MQTT Protocol. If the unidentified user in the webcam is detected by this device, the information in the cloud will be set to ' off ' status, allowing light and fan to be switched off. Although Passive Infrared Sensor (PIR) is widely used in home automation systems, PIR sensors detect heat traces in a room, so they are not very sensitive when the room itself is hot. Therefore, in some countries such as INDIA, PIR sensors are unable to detect human beings in the summer. This system is an alternative to commonly used PIR sensors in the home automation process.

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 Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

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 Automatic Attendance System Using Face Recognition Technique

2020 ◽  
Vol 9 (1) ◽  
pp. 2134-2138
Keyword(s):  
Face Recognition ◽  
User Interface ◽  
Graphical User Interface ◽  
Cascade Algorithm ◽  
The Face ◽  
Haar Cascade ◽  
Recognition Technique

Attendance system is very important in schools and colleges’ Manual attendance system has many difficulties like it may less accurate and critical to maintain. So, attendance system using face recognition technique increase the accuracy and also it required less time than other methods. There are many existing system for attendance such as face recognition using IoT, PIR sensors and so on. For face recognition, hardware devices also helpful. But challenge is that to maintain all the sensors properly without get damage. After studying all method and techniques we are trying to implement a system with Haar Cascade Algorithm which has highest accuracy among all. It is able to capture the images from 50-70cm. We are creating graphical user interface which capture the images, create the dataset and train the dataset on single click. After recognizing the face it will display name of student and roll number. That information stored in attendance sheet automatically with time and date.

scholarly journals An Analysis of Face Recognition under Face Mask Occlusions

2021 ◽  
Author(s):  
Susith Hemathilaka ◽  
Achala Aponso
Keyword(s):  
Face Recognition ◽  
Rapid Development ◽  
Face Mask ◽  
Large Area ◽  
Daily Lives ◽  
Lighting Conditions ◽  
Intractable Problems ◽  
The Face ◽  
Recognition Systems ◽  
Pose Variation

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.

Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

2021 ◽  
pp. 185-207
Author(s):  
Priyank Jain ◽  
Meenu Chawla ◽  
Sanskar Sahu
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Research Work ◽  
Modern World ◽  
The Face ◽  
Haar Cascade

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.

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