Haar Cascade Algorithm Research Articles

Discover and safe: an automated security management system for educational institutions

In this paper, we proposed discover and safe (DaS) automatic security system by the help of face recognition. The focus of this automated system is to provide high-level security to manage the entry of people with face detection. This paper proposes haar cascade algorithm with dip libraries to create a camera-based real-time security management system through face detection and recognition. Haar cascade algorithm is an object detection algorithm which is mainly used in identifying face of any image or a real-time video (by webcam or building camera). The DaS framework worked on two phases: 1) to locate whether the VM is an unintended security; 2) to secure mission critical applications. The DaS access system implemented the face encoding scheme to detect the face and eye which works effectively on light and illumination changes. The results show that DaS framework can Armor the VM from obscured security problems and steal hidden doors against the attackers.

An Automated Attendance System Using Facial Detection and Recognition Technology

The proposed system utilizes Haar Cascade algorithms for facial detection and recognition. The system accurately detects and recognizes individuals based on their unique facial features. Facial detection algorithms identify and extract facial regions from input images or video frames, isolating the necessary facial details for further analysis. Subsequently, facial recognition algorithms, LBPH, compare these features with pre-registered faces stored in the system's database, calculating confidence scores to determine individual identities. The system incorporates a user-friendly interface, enabling administrators to easily manage attendance records. They can effortlessly add or remove students from the system's database, access attendance reports, and monitor real-time attendance data. The proposed Attendance Management System revolutionizes the conventional attendance tracking process by offering enhanced accuracy, efficiency, and security while providing real-time monitoring and comprehensive reporting capabilities. With the potential for adoption in various educational institutions, organizations, and industries, this system represents a significant advancement toward streamlined and intelligent attendance management. Keywords: face detection, face recognition, attendance, LBPH

Identification of Teacher Interaction in Online Class

Abstract: The system investigate teacher’s instructional activities from live audio of online class. The system is trained using python to identify the occurrences of eight key instructional activities from teacher audio. The system was able to identify eight key instructional activities using speech recognition techniques with an eye towards providing automatic feedback to teachers. One advancement in the field of automation is the Automated Attendance system replacing the old and traditional attendance marking. This system is based on the face detection and face recognition algorithms. The concept of this paper is to provide real time teacher attendance to the faculty’s data base. Automatically detects the teacher face using the web camera and detect the facial part using Haar cascade algorithm and the image undergoes the various techniques and will compare with reference image, Later the attendance of the teacher is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.

Arduino Based Smart and Remote Voting System with Smart Card Implementation and Dual Biometric Authentication

Abstract: Democracy is a synonym for progress of the nation. Improving voting numbers will boost in overall development of the country. To achieve this task, we propose an IoT based remote electronic voting system, provided with dual biometric authentication, composed of fingerprint verification and face recognition. Instead of paper based identification card, we intent to implement an RFID based smartcard with details of the voter stored in the card, including biometric data saved alongside information like name, age, locality, constituency and even aadhar identification number. This model uses face recognition using Haar cascade and LBPH algorithms, set up using OpenCV libraries. The EVM system captures the face data of the voter, verifies by comparing it with existing face datasets, and upon validation will be allowed to cast their vote.

Real-Time Face Mask Detection

After the breakout of the worldwide pandemic COVID-19, there arises a severe need of protection mechanisms, face mask being the primary one. According to the World Health Organization, the corona virus COVID-19 pandemic is causing a global health epidemic, and the most successful safety measure is wearing a face mask in public places. Convolutional Neural Networks (CNNs) have developed themselves as a dominant class of image recognition models. The aim of this project is to use machine learning capabilities for detecting and recognize face masks worn by people in any given video or picture or in real time. This project develops a real-time, GUI-based automatic Face detection and recognition system. It can be used as an entry management device by registering an organization's employees or students with their faces, and then recognizing individuals when they approach or leave the premises by recording their photographs with faces. The proposed methodology makes uses of Principal Component Analysis (PCA), Face Net, LBPH Face detection Algorithm and HAAR Cascade Algorithm. Based on the performance and accuracy of our model, the result of the binary classifier will be indicated showing a green rectangle superimposed around the section of the face indicating that the person at the camera is wearing a mask, or a red rectangle indicating that the person on camera is not wearing a mask along with face identification of the person. Once the Algorithm Identified the face it will send a text message to the recognized person to wear a mask and save the record.

Face Recognition and Automatic Attendance Management System using Machine Learning Techniques

An automated attendance management system maintains a record of the attendance of a particular student, whether he/she is present in the class. The most important element of the virtual platform for students is maintaining attendance in an educational institution. When done manually, it wastes a substantial amount of time and effort. Due to a lack of a reliable attendance mechanism, there were also numerous fraudulent attendances. The face is the most significant feature that distinguishes a person. As a result, the focus of this study is on an attendance system that uses a real time face identification method based on the frontal face recognition idea, as well as an efficient Haar cascade algorithm based on an open-source image processing framework as well as the Local Binary Pattern Histogram.

License plate detection using AI

In today's world, withincreasing population number of vehiclesare also increased. Monitoring each andevery vehicle in roadside by manualforce becomes a tedious job. For effective traffic management some solution is required. The traditional system of licenseplate recognition basically relies on themorphological processing of images. The accuracy of recognition is also lower. Inorder to provide an efficient solution forlicense plate position, we propose a new method of license plate recognition. In the license plate position, we use traditional positioning method and Haar Cascade algorithm to detect the licenseplate. In addition with the traditionalidentification system cutting and matching, we use the ability feature extraction of Convolution Neural Network (CNN) to detect the license platedirectly, which avoids the recognitionerror which is caused by the segmentation in the license platerecognition. In this project, we propose a new license plate recognition system. It is divided into two parts, they are licenseplate positioning and characterrecognition.

Automated Body Temperature, Mask Detection And Face Recognition Based Attendance System

Cross-contamination of the virus among employees is a major risk factor as communities grapple with the COVID-19 outbreak. Swiping cards or bio-metrics are commonly used to track individual employee attendance. This can be a simple means of infection transmission, leading to the virus's spread. If the person is not wearing a mask that can lead to a serious spread of virus. In order to avoid this physical contact for measuring temperature a non-contact temperature gauge can be implemented. This can be used as part of a first examination at entry points to know or avoid people with high temperature to enter the premises, which could also help in order to classify a person who has contracted the COVID-19 infection. This system can be used to as a replacement for the current system, in which a staff is manually scans temperature were the staff is exposed completely to the outsider. The proposed system uses the Haar cascade algorithm for face recognition and temperature of the person is measured along with attendance updation. This eliminates the possibility of virus transmission via card swipes or bio-metrics. The device also identifies whether the employee is wearing a mask and guarantees that the COVID necessary masking protocol is followed

Accident Prevention by Detecting Drivers Fatigueness

Abstract: Various studies indicate that fatigueness in drivers leads to road accidents. It can lead to serious injuries like brain damage or it can lead to death. Therefore preventing people from harming the countermeasure device is necessary as a reliable solution. This study therefore came up with a new way of explaining the driver's fatigueness. This example uses the Haar Cascade algorithm, next to the OpenCV library to keep an eye on the real-time video of the driver and critique the driver's eyes. The Eye Aspect Ratio (EAR) is used in this measurement device to determine if the eyes are open or closed. The Mouth Aspect Ratio (MAR) is also used as an important element while this model describes the driver's fatigueness as the driver begins to yawn just before the driver feels fatigue. If the driver is found to be fatigue, a warning signal is issued. Keywords: OpenCV Automated algorithms, database, Image recognition, face detection, frontal postures, extraction phase, OpenCV, data models, HaarCascade classifier, training set.

Design of Face Recognition Security System on Public Spaces

<p class="Abstract">Implementation of physical security such as recruitment of security officers, installation of CCTV, and restrictions on public access have become commonplace nowadays. Computer systems equipped with archival storage media must be properly maintained, including computer systems containing sensitive information that must be stored in a locked and secure place. This study applies a security system using facial recognition to determine who is authorized over the data in the computer system—using Haar Cascade as a face detector and LBPH as a match between faces that can access and those that are not on the list. On the other hand, if the person is unidentified, preventive measures will be performed. Based on the result, the proposed system using Raspberry Pi 4 is able to identify a face using Haar Cascade algorithm with an accuracy of 68% and average duration process of 0.392s, and able to recognize face using LBPH algorithm with accuracy between 50.74% to 100% and average duration process of 0.548s.<em></em></p>

DRIVER DROWSINESS DETECTION FOR BEHAVIORAL ANALYSIS

Driver drowsiness is the leading cause of traffic accidents; thus, drowsiness detection plays an important role in preventing them. By developing an automatic solution for alerting drivers of drowsiness before an accident occurs, the number of traffic accidents could be reduced. As a result, this study suggests a method for detecting drowsiness in real time. Image processing and machine learning are two aspects of the suggested approach. The purpose of the image processing step is to recognize the driver's face and then extract the image of the driver's eyes. This phase employs the Haar face detection method, which takes video as an input and outputs the discovered face. Next, haar is utilized to extract the detective's eye image, which will be used as an input for the machine learning phase. The role of machine learning, in this case, is to classify whether the driver's eyes are closed or open using a support vector machine. If the classification results show that the driver's eye is closed for a predetermined amount of time, the driver's eyes will be regarded closed, and an alarm will be activated to inform the driver. On available benchmark data, the proposed methodology has been tested. The results show that the hybridized image processing approaches with machine learning techniques are accurate and resilient. As a result, it can be concluded that the presented methodology is a viable solution method for detecting driver tiredness in the real world. Keywords- Drowsiness detection: Face detection; Eye detection; Yawn detection; Eye aspect ratio; Haar cascade algorithm.

Face recognition based attendancemanagement system

In this modern era, the technology plays an important role . Face recognition technology is one of the most widely used . It can be used for either in security, authentication, identification, like in phones also this technology is used very widely to unlock the phone. Aside from having low accuracy compared to the detection of the iris and the recognition of fingerprints, it is widely used due to its transthoracic and transthoracic procedure. In addition, this technology is also used in various places like school,colleges,hospitals and many other places. This technology is generally used for schools and colleges to provide the facility to the teacher like to decrease the burden of the teachers because it is very time consuming to take the attendance with the help of the pen paper based so it is less time consuming. And there may be opportunities for the presence of a representative. Thus, the demand for this program is growing. The program consists of sections consisting of archival construction, facial recognition, facial recognition, attendance development. The software is created by pictures of students in the classroom. Face detection software are performed using the Haar-Cascade algorithm respectively.

Real time COVID-19 facemask detection using deep learning

The COVID-19 pandemic is producing a global health pandemic. According to the World Health Organization (WHO), the utmost effective protection is to wear a face mask in crowded regions/areas. During this pandemic, it is compulsory for every person to wear a mask and maintain social distancing. In the field of Image Processing, Convolutional Neural Networks (CNNs) have risen to prominence as the most common type of image realization/recognition model. Our project's purpose is to research and assess Machine Learning (ML) technologies for identification and recognition of people wearing face masks in any pre-recorded videos, photos, or in actual-time (real- time) circumstances. Our project aims to create a real-time Graphics User Interface based Automated Facial Recognition as well as Mask Detection System. The algorithms used in the proposed methodology are Principal Component Analysis (PCA) and the HAAR Cascade Algorithm. Finally, the result is indicated by a “GREEN” color rectangle box, which would be drawn around the section of the face, which indicates that the person on the camera is wearing a mask, or a “RED” color rectangle box, which indicates that the person on the camera is not wearing a mask. This model achieves 99% accuracy.

Library Reservation System Using Face detection

Today, Covid-19 has completely changed our way of life. The new generation has made people stay at home, instead of going on vacation. The users are not allowed in a close place, especially in a building or room. The library that used to be packed with people reading books, studying, and using the computer is now becoming empty. The room in the library has been limited only to a certain number of people to prevent any dangerous situation regarding the Covid-19 virus to spread. The room needs to be reserved beforehand for the user to use. This situation has become a problem for users as the user’s desired room may be occupied by other users. Thus, Reservation System using face recognition for the library was developed to overcome this situation. In this paper, the researcher will use the Haar Cascade Algorithm to scan the face and MySQL as a database to detect the room and time slot for a reservation. Phyton language and Visual Studio Code were used to develop the system. The limitation of this project is that the face registration took a long time for some users because of the lightning that makes it the system hard to recognize the face. The recommendations for future work are to use a high technology camera to scan the face and construct an admin page because the system does not have an admin page.

Real Time Face Mask Detection-A Survey

After the breakout of the worldwide pandemic COVID-19, there arises a severe need of protection mechanisms, face mask being the primary one. According to the World Health Organization, the corona virusCOVID-19 pandemic is causing a global health epidemic, and the most successful safety measure is wearing a face mask in public places. Convolutional Neural Networks (CNNs) have developed themselves as a dominant class of image recognition models. The aim of this research is to examine and test machine learning capabilities for detecting and recognize face masks worn by people in any given video or picture or in real time. This project develops a real-time, GUI-based automatic Face detection and recognition system. It can be used as an entry management device by registering an organization’s employees or students with their faces, and then recognizing individuals when they approach or leave the premises by recording their photographs with faces.The proposed methodology makes uses of Principal Component Analysis (PCA) and HAAR Cascade Algorithm. Based on the performance and accuracy of our model, the result of the binary classifier will be indicated showing a green rectangle superimposed around the section of the face indicating that the person at the camera is wearing a mask, or a red rectangle indicating that the person on camera is not wearing a mask along with face identification of the person. Face detection and face recognition are very important technologies these days, furthermore we noticed that they got have a variety of uses such as cellphones, army uses, and some high risk information offices. We decided to make a device that detects and recognize the face as a student attendance system and can be a substitute for the regular paper attendance system and finger print attendance system. The main function in our project is going to be done using LabVIEW because, LabVIEW is a very helpful programming tool in regards of facial uses and very helpful in other uses. Our project is based on a main program in LabVIEW that detects and recognize faces with giving scores and parameters, furthermore the subsystems are an Excel sheet that is integrated with the program, and a messaging device that is for either a message for absent students or to the student’s parent.

Real Time Face Mask Detection-A Survey

After the breakout of the worldwide pandemic COVID-19, there arises a severe need of protection mechanisms, face mask being the primary one. According to the World Health Organization, the corona virusCOVID-19 pandemic is causing a global health epidemic, and the most successful safety measure is wearing a face mask in public places. Convolutional Neural Networks (CNNs) have developed themselves as a dominant class of image recognition models. The aim of this research is to examine and test machine learning capabilities for detecting and recognize face masks worn by people in any given video or picture or in real time. This project develops a real-time, GUI-based automatic Face detection and recognition system. It can be used as an entry management device by registering an organization’s employees or students with their faces, and then recognizing individuals when they approach or leave the premises by recording their photographs with faces.The proposed methodology makes uses of Principal Component Analysis (PCA) and HAAR Cascade Algorithm. Based on the performance and accuracy of our model, the result of the binary classifier will be indicated showing a green rectangle superimposed around the section of the face indicating that the person at the camera is wearing a mask, or a red rectangle indicating that the person on camera is not wearing a mask along with face identification of the person. Face detection and face recognition are very important technologies these days, furthermore we noticed that they got have a variety of uses such as cellphones, army uses, and some high risk information offices. We decided to make a device that detects and recognize the face as a student attendance system and can be a substitute for the regular paper attendance system and finger print attendance system. The main function in our project is going to be done using LabVIEW because, LabVIEW is a very helpful programming tool in regards of facial uses and very helpful in other uses. Our project is based on a main program in LabVIEW that detects and recognize faces with giving scores and parameters, furthermore the subsystems are an Excel sheet that is integrated with the program, and a messaging device that is for either a message for absent students or to the student’s parent.

A Survey on Touchless Heart Rate Measurement Using Facial Expression for Covid Patient

Abstract: This research describes both a touch-free heartbeat detection system and a cardiopulmonary signal modeling technique. A vector network analyzer is used to test a microwave system for detection of a heartbeat signal at a distance of 1 m from a person. The developed system can detect heartbeat signals and adjust their frequency and strength. Measurements are taken at 2.4, 5.8, 10, 16, and 60 GHz, as well as at power levels ranging from 0 to -27 dBm. Based on data for both breathing and heartbeats, a model of the recorded signals reflecting cardiopulmonary activity is provided. The heartbeat rate and variability are obtained from the modeling signal using wavelet and classic filters. 0and-20dB Keywords: Haar cascade algorithm, Image processing, cardiopulmonary signal modeling technique, Testing.

Application of Artificial Intelligence in Monitoring the Use of Protective Masks

<div><p class="4">In the context of current epidemic diseases, this study developed a web application, which can monitor the use of protective masks in public environments. Using the Flask framework in Python language, the application has a control panel to help visualize the obtained data. In the detection process, Haar Cascade algorithm is used to classify faces with and without protective masks. Therefore, the web applications are lightweight, allowing the detection and storage of images captured in the cloud and thte possibility of further data analysis. The classifier presents precision, reversal and f-score of 63%, 93% and 75%, respectively. Although the accuracy is satisfactory, new experiments will be carried out to explore new computer vision technologies, such as the use of deep learning.</p></div>

Social Distancing for Covid-19 Monitoring System

The battle of corona-virus and mankind is possible to be tackled as long as we maintain the basic norm of social distancing and wearing masks amongst ourselves as it is through our droplets from the respiratory tract that the virus spreads. With the increasing demand for man-force and people requiring to go to their workplaces post lockdown, it is very necessary that we save each other from the virus. In this project, we will go through a detailed explanation of how we can use Python, AI and Deep Learning to monitor social distancing at public places and workplaces are keeping a safe distance from each other by analyzing real-time video streams from the camera and also detect facial mask monitoring using OpenCV and Python. To ensure if people are following social distancing protocols in public places and workplaces, we wanted to develop a tool that can monitor if people are keeping a safe distance from one another, wearing masks or not by processing real-time video footage from the camera. People at workplaces, factories, shops can integrate this tool into their security camera systems and can monitor whether people are keeping a safe distance from each other or not along with that we detect facial mask monitoring using Python with help of haar-cascade algorithm to see whether a person is wearing a mask or not. We are also planning to include thermal screening detection to measure the temperature of the subjects, a dashboard which will display a live report of corona cases around the world. We will also include an alert system that will send a notification to the authorities if the social distancing is not followed or if the temperature exceeds the threshold. The authorities can take suitable measures to isolate the subject and thus prevent the spread of Covid-19.

Face Mask and Social Distancing Detection Using ML Technique

Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.