Haar Cascade Classifier Research Articles

Detecting Objects Using Haar Cascade for Human Counting Implemented in OpenMV

Sight is a fundamental sense for humans, and individuals with visual impairments often rely on assistance from others or tools that promote independence in performing various tasks. One crucial aspect of aiding visually impaired individuals involves the detection and counting of objects. This paper aims to develop a simulation tool designed to assist visually impaired individuals in detecting and counting human objects. The tool's implementation necessitates a synergy of both hardware and software components, with OpenMV serving as a central hardware device in this study. The research software was developed using the Haar Cascade Classifier algorithm. The research process commences with the acquisition of image data through the OpenMV camera. Subsequently, the image data undergoes several stages of processing, including the utilization of the Haar Cascade classifier method within the OpenMV framework. The resulting output consists of bounding boxes delineating the detection areas and the tally of identified human objects. The results of human object detection and counting using OpenMV exhibit an accuracy rate of 71%. Moreover, when applied to video footage, the OpenMV system yields a correct detection rate of 73% for counting human objects. In summary, this study presents a valuable tool that aids visually impaired individuals in the detection and counting of human objects, achieving commendable accuracy rates through the implementation of OpenMV and the Haar Cascade Classifier algorithm.

Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN

Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. Dlib is chosen to process with Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). It loads the pre-trained model, computes the HOG features, slide the window scanning at different scales, classify the windows using the SVM classifier, generate bounding boxes, and applying non-maximum suppression. ResNet50 architecture is employed to recognize face and Convolutional Neural Networks (CNN) is applied to recognize facial expression. The training accuracy is 70% and validation accuracy is 60%.

Localizing Face Recognition with Haar-Cascade Classifier and LBPH using Python

This paper experimented with the combination of the Haar-Cascade Classifier and Local Binary Pattern Histogram in creating a Face Recognition System using Python programming language. Images were gathered using Python script from the 10persons selected while facing the camera. Each individual had 1000 photos that were used to train a model. Another Python script was created for training, identification and recognition. The overall efficiency rating of the model was 84% which denotes a practical recommendation for utilization. The findings of this study contribute as a reference for further development of face recognition with other image classification algorithms

YOUTUBE VIDEO RECOMMENDATION BASED ON FACIAL EXPRESSION

Human faces play a significant role in determining a person's mood. Emotion plays an important role in various fields, including biomedical engineering, brain science, neuroscience, psychological wellness, and mental health. Emotions are not only used for determination of the state of the human brain but too utilized as a proposal framework to help people in finding things that coordinate their needs and inclinations. This motivated us to create a framework that can viably and proficiently recognize feelings from the facial expressions of the user and recommend the YouTube videos based on that emotion. Emotions are bought by the neurophysiological changes that are associated with thoughts, feelings and behavioral changes in a person. Every emotion needs to be treated in a right way and research suggests that watching YouTube videos influences your mood, and in our experiment, we try to influence it in a good way. We can detect changes in emotion by observing facial expressions, body language, and tone of voice. There are many indicators of a person's mood, but facial expressions are one of the most important. In our experiment, we used the Haar Cascade Classifier to detect the face and a convolution neural network to analyze the facial expression. Performance of our proposed model is 95.66% for face detection and 61.88% for emotion detection. Key Words: Emotion Detection, FER-2013, HAAR Cascade, YouTube Recommendation, CNN.

Green Software Engineering: Cloud-based Face Detection and Static Code Analysis

This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential flaws, and identifies code odors. By integrating these practices, the proposed method seeks to reduce resource consumption, maximize energy efficiency, and enhance code maintainability, promoting environmentally friendly and sustainable software engineering practices. One outcome of our effort was creating the YasminNadiaArabcSocialMediaImages data collection, which includes faces of Arabic social media celebrities and is filled out to be accessible for public usage on the websites Kaggle and GitHub.

Face Recognition Based on Computer Vision: A New Way of Attendance Marking

Computer Vision marked a significant contribution to the application of automated attendance marking. This technology captures the face of an individual facing in an image-capturing device such as a camera. This study developed a face recognition attendance marking based onthe machine learning method. Computer Vision libraries from OpenCV such as Haar Cascade Classifier and Local Binary Pattern Histogram were used for identification and recognition. Images were collected from 8 participating individuals. The images were clustered according to their name (label). These images were used for training to create a face recognition model. Real-time testing was performed to evaluate the system’s performance. The results generated a mean recognition accuracy of 95% which implies a significant basis for the application of the system to attendance marking

“Tec-House” WEBCAM-BASED REMOTE SENSING SYSTEM FOR HOME AND BUILDING SECURITY USING THE HAAR CASCADE METHOD

A home security system is something that every home owner must pay attention crimes such as burglary often put homeowners at risk. Therefore we need a tool that can bring together automatically remotely to protect the house. The system worked on in this article is a remote sensing system based on webcam. The method used in this sensing system uses the haar cascade classifier method. The results obtained from this remote sensing system are for the implementation of the system on homeowner data sets with 98% results, while for non-home owner image data sets with 96% results. From the results of using a webcam-based remote sensing system using the Haar Cascade Classifier method it can be implemented properly and the average error is 97%. The existence of this Tec-House tool can reduce the crime of theft in a house or building.

A Machine Learning Model for Flaw Identification in Fibre-Reinforced Composites

A Haar cascade classifier is a machine learning (ML) algorithm used for object detection. In this paper, the Haar algorithm is introduced in the context of a non-destructive evaluation of fibrereinforced composite (FRC) structures. The Haar learning model is used for flaw identification from thermal images. Thermal images are created from cross-ply (CP) carbon fibre-reinforced laminates with flat-bottomed holes (6–10 mm) of different depths from the surface (0.5–1.5 mm). After training is complete, the model successfully detects similar artificial flaws in previously unseen thermal images. In doing so, the feasibility of Haar classifiers for automatic evaluation of FRCs is established.

FACE RECOGNITION MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER DAN LOCAL BINARY PATTERN HISTOGRAM

Increasing threats to data security is a consequence of technological advances that continue to develop, especially with the existence of technology that allows data access remotely. It is important to always maintain data security and take preventive measures to prevent data theft. The author proposes the use of facial recognition biometric technology. Face Recognition is one of several biometric technologies that can be used for identity verification systems. The data used amounted to 5 classes, classes with each class having 250 facial images, the total data used amounted to 1250 facial images, consisting of 1000 training data (train) and 250 test data ( tests). The shooting process is done automatically, the system automatically takes 250 faces per class. The next stage is feature extraction using the Local Binary Patterns Histograms (LBPH) method. Furthermore, the system will recognize the detected faces with those in the dataset. The use of the Haar Cascade Classifier and Local Binary Pattern Histogram (LBPH) methods to detect faces using 250 test data obtains an accuracy value of 92%. Testing in real time was carried out 75 times with a distance of 30 cm, 50 cm and 100 cm. The use of the Haar Cascade Classifier and Local Binary Pattern Histogram (LBPH) methods to detect faces in real time obtains an accuracy of 90%.

AI-based face recognition system with telegram notification for room security on raspberry PI

This research is based on the importance of a security system in a room by implementing AI combined with the telegram notification system. The goal is that security information can be obtained quickly and in real-time. The methodology used is to design a hardware system consisting of input, process and output devices. The input device consists of a Logitech C270 camera mounted on 2 MG966R type servo motors so that the camera can rotate on the X and Y axes, then the results of the camera captures are processed using the Haar Cascade Classifier and Local Binary Pattern Histogram (LBPH) algorithms. Raspberry Pi 4 is used as a data processing center and push notification to telegrams in the form of images when faces are detected by a web camera. Only registered users may enter the room, by opening the door when a face is recognized. Our findings show that a room security system with an AI-based facial recognition application can be implemented, according to the planning and design results in this study. The door opening process produces an average result of 4.586 seconds, with the longest time being 4.981 seconds and the fastest time being 4.116 seconds. The door closing process produces an average result of 4.496 seconds, with the longest time being 4.966 seconds and the fastest time being 4.106 seconds. The average time of opening and closing the door is ideal and safe. From the results of the research that has been done, it can be concluded that the use of AI in this study aims to make decisions that only registered users can enter a room. In addition, the ability of the camera to move dynamically on the x and y axes is one of the system developments that did not exist before, so that the ability to take pictures besides being more accurate also becomes wider dynamic.

Artificial Intelligence Based Domotics Using Multimodal Security

All electronic devices in our cutting-edge technology world must be networked together via the Internet if users want to have remote access to them. As a result, it may raise a variety of serious security issues. This study suggests a remote access home automation security system that incorporates utilizing the Internet of Things (IoT), and Artificial Intelligence (AI) for ensuring the security of the house. For a highly efficient security system, Face recognition has been used to maneuver the door access. In case of power outage or for any technical issues, an alternative security PIN has been added which is only accessible by the owner. Moreover, individuals are able to monitor and control the door access along with other attributes of the house using an application. In this work, Face detection is performed using the Haar Cascade classifier, while face recognition is performed using the Local Binary Pattern Histogram (LBPH). 95.7% accuracy in recognizing faces has been achieved after evaluating the proposed system.

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results.

A heuristic technique for music recommendation using Haar cascade classifier through facial expressions

Human emotion assumes a fundamental part lately. Feeling depends on human sentiments which can be both communicated or not. Feelings communicate the human's singular way of behaving which can be in various structures. Extraction of the emotion states people individual condition of conduct. The goal of this task is to separate element from human face and identify feeling and to play music as indicated by the emotion identified. In this task, fostering a model to suggest dynamic music proposal framework in view of human feelings is our principal perspective. From the genuine face the emotion is recognized and when the mind-set is gotten from input picture, particular melodies for the state of mind would be played to hold the clients. In this methodology, the application gets associated with human sentiments accordingly giving an individualized touch to the clients. Our projected framework focuses on distinguishing the human face using Haar Cascade classifier. A few stages like face location utilizing webcam, facial feature extraction, look identification, with that interfacing with a music player will be followed. For exploratory outcomes, Haar cascade calculation and Computer vision along with CNN (Convolution Neural Network) for feeling discovery and music suggestion.

Face Recognition Based Attendance System

Abstract: If attendance is managed manually, it can be quite taxing on the teachers. Smart and automated attendance management systems are being used to address this issue. This architecture makes it simple to address the issue of proxies and students being counted as present even though they are not. The system implemented makes use of the LBPH face recognizer to instantly recognise a person's face. Light has an impact on both Eigen and Fisher faces, and real-world lighting conditions cannot always be ideal. This issue can be solved by a change in the LBPH faces. This method decides who is present and who is not by comparing the images from the test and training sessions. A system-updated excel sheet that contains the attendance information is kept in the database. The faculty can get a copy of the attendance sheet through mail by clicking auto mail button in the GUI. The face recognizer detects faces using Haar Cascade Classifier, and recognizes the faces using Local Binary Patterns Histograms (LBPH) algorithm.

Advanced High Secured Smart ATM System

Abstract: Automated Teller Machines are widely used nowadays by people. But It’s hard to carry their ATM card everywhere, people may forget to have their ATM card or forget their PIN number. The ATM card may get damaged and users can have a situation where they can’t get access to their money. In our proposal, use of biometrics for authentication instead of PIN and ATM card is encouraged. Here, The Face ID is preferred to high priority, as the combination of these biometrics proved to be the best among the identification and verification techniques. The implementation of ATM machines comes with the issue of being accessed by illegitimate users with valid authentication code. This project provides service to the user only when the user is legitimate or the user is verified by the legitimate user of the ATM card. The users are verified by comparing the image taken in front of the ATM machine, to the images which are present in the database. If the user is legitimate the new image is used to train the model for further accuracy. This system uses openCV to process the image being obtained and Haar Cascade Classifier to detect the faces in the image. The face recognition is done using Local Binary Pattern.To overcome these problems,the project ’ATM Security system based on Face recognition, PIN and OTP’ consists of conventional features ie is Personal Identification Number (PIN) along with additional features like face recognition and one-time password (OTP) is used. Database holds information about a user’s account details, images of his/her face and a mobile number which will improve security to a large extent.

Face Recognition Based Attendance System

In colleges, universities, organizations, schools, and offices, taking attendance is an important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one making our task easy. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, section, and photographs, is trained and is also available for teachers and staff attendance. The images are extracted using Open CV a library of python. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.

Motion Detection and Multiple Faces Identification using Webcam

As technology is marking the highest peak, all things are being automated using Machine Learning and Deep Learning. By using Machine Learning models, we can detect the movements, actions, multiple faces, their recognition and many more in snap of time. Just like that, this project is implemented using Machine Learning models, like OpenCV for detecting motions of an object and Face Recognition for multiple faces recognition to provide the advanced security. In this paper, we are going to see the project named as “Motion Detection and Multiple Face Identification using Webcam” were detection of motion and multiple faces will be done with the help of a system Webcam or Security Cameras. This is implemented using Python’s OpenCV a Computer Vision module and Cascade Classifier. This research paper will guide, how this machine learning project helps to detect and verify the motion and faces of people more efficiently and provide advanced security to an organization or to a home. Key Words- Machine Learning (ML), Artificial Intelligence (AI), MYSQL, Haar-Cascade Classifier, Gaussian-Blur, Face Recognition, MySQL Database.

Survey on Vision Based Hand Gesture Interface for Controlling Multimedia Player

As computers become additional pervasive in society, facilitating natural human–computer interaction (HCI) can have a positive impact on their use. Hence, there has been growing interest within the development of recent approaches and technologies for bridging the human-computer barrier. the last word aim is to bring HCI to a regime wherever interactions with computers are going to be as natural as associate degree interaction between humans, and to the current finish, incorporating gestures in HCI is a crucial analysis space. Gestures have long been thought-about as associate degree interaction technique that may doubtless deliver additional natural, creative, and intuitive strategies for human activity with our computers. Hand gesture recognition is one amongll|one amongst|one in every of} the systemsthat may notice the gesture of the hand in a period of time video. The gesture of hand is classed inside a definite space of interest. during this study, planning hand gesture recognition is one among the difficult jobs that involves 2 major issues. foremost is that the detection of the hand. Another drawback is to form an indication that's appropriate to be used one hand at a time. This project concentrates on however a system might notice, acknowledge and interpret hand gesture recognition through computer vision with the difficult factors that variability within the create, orientation, location, and scale. To perform well for developing this project, differing kinds of gestures like numbers and sign languages got to be created during this system. The image taken from the period of time video is analyzed via Haar-cascade Classifier to notice the gesture of hand before the image process is finished or in different words to notice the looks of hand in a very frame. during this project, the detection of hand are going to be done mistreatment the theories of Region of Interest (ROI) via Python programming. the reason of the results are going to be targeted on the simulation half since the distinction for the hardware implementation is that the ASCII text file to scan the period of time input video. the event of hand gesture recognition mistreatment Python, OpenCV, and YOLO V3 will be enforced by applying the theories of hand segmentation and also the hand detection system that uses the Haar-cascade classifier

Sign Language Translator Based on Raspberry Pi Camera Using The Haar Cascade Classifier Method

Sign language is the main tool of communication for people with hearing impairments. Communication is very limited and difficult to understand between normal people who do not know sign language, so an interpreter is needed. Where not everyone, even a few normal people, learns sign language, especially the Indonesian Sign Language System (SIBI). Motion Detection is an important subject in the field of computer vision, which is used by many systems. Today's Internet of Things is very helpful and facilitates daily human activities. An internet network allows a device to be controlled from a considerable distance. This study described a sign language translator tool for the deaf and speech impaired using a raspberry-pi camera and displayed it on the other device monitor. This system was built using the Python programming language and the OpenCV Library. The system is using Haar Cascade Classifier algorithm, where there will be data on all hand shapes based on the letters to be translated. This application uses the OpenCV library and Visual Studio Code IDE software connected to the Raspberry Pi Camera. The publisher will send data to other devices using the MQTT Broker to connect and display detection results to other device monitors wirelessly using a local network. The research was conducted at various distances between the hand and the webcam, from 30cm to 150cm. The research results using the Haar Cascade Classifier method to detect sign language obtained an accuracy of 82%.

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

Door lock provides numerous benefits and has become indispensable in daily life as it acts as a security guard to prevent burglars and protect home belonging safely. The unlock methods of existing door lock system widely use keys and thumbprint involve touching the object may cause the spread of COVID-19. In this paper, a computer vision based security door lock system using Raspberry Pi (called SAFE) is proposed. Haar-Cascade classifier is employed as face detection classifier, while Local Binary Pattern Histogram (LBPH) is proposed as face recognition classifier. Recognition result is processed based on the usage of user to provide insights of SAFE. The accuracy of SAFE using pre-trained LBPH classifier achieves average of 86% based on the data obtained. The recognition speed outperforms existing work using principal component analysis and eigenfaces.