Haar Cascade Classifier Research Articles

РОЗПІЗНАВАННЯ ОБЛИЧ В РЕАЛЬНОМУ ЧАСІ ЗА ДОПОМОГОЮ БІБЛІОТЕКИ OPENCV ТА МОВИ ПРОГРАМУВАННЯ PYTHON

The article discusses the process of face recognition, which is one of the key tasks in the field of computer vision. The main focus is on the use of the OpenCV library, which provides tools for efficient and fast face detection in images and real-time video. The paper describes the Haar cascade and the Viola-Jones algorithm, which form the basis of the face detection implemented in OpenCV. It also discusses the step-by-step stages of project implementation in the Python programming language, as well as image processing. The results of experiments demonstrating the effectiveness and accuracy of the proposed approach are presented. The article will be useful for developers, engineers, and researchers interested in implementing face recognition technologies in their projects using basic computer vision algorithms. In addition to the fundamental techniques, the article delves into the nuances of optimizing the face detection process. This includes adjusting parameters such as the scale factor and the minimum number of neighbors, which are crucial for balancing detection accuracy and performance. The importance of preprocessing steps like grayscale conversion and using the correct type's cascade file is also highlighted, as these steps significantly enhance the detection rate by improving image contrast. The theoretical implementation section provides a step-by-step conception of face detection and recognition using the Haar cascade and Viola-Jones method. Also, need to underline the importance of this open topic about the recognition of human emotions using the research by Robert Plutchyk. He determines the main human emotions that are the basis for combining the recognition of a person and his emotions. The practical implementation section provides a realization of face detection and recognition using OpenCV and Python: loading image data, applying the Haar cascade classifier, and testing the program solution. To validate the effectiveness of the approach, several experiments were conducted using diverse image datasets. The results, summarized in tabular format, indicate a high detection rate with minimal input data. The experiments also compare the performance with different counts of input data, highlighting the advantages and disadvantages of the Viola-Jones method.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

SMART ATTENDANCE WITH FACE ANTI-SPOOFING TECHNOLOGY USING HAAR CASCADE CLASSIFIER

Traditional attendance systems often encounter challenges in efficiently and accurately recording attendance. This research aims to introduce an innovative solution through the development of an intelligent anti-spoofing attendance system based on facial recognition using the Haar Cascade Classifier method. Designed to overcome the inefficiencies in attendance recording, this system ensures the accuracy of educational staff attendance records. Its development method relies on the Haar Cascade Classifier, employing image processing to detect learned object features, particularly focusing on facial recognition. Research findings indicate that the implementation of this system achieves an average accuracy rate of 98.90% in attendance recording. The facial recognition technology ensures reliable attendance recording with confidence levels exceeding 80%, signifying precise facial identification that addresses various challenges and ensures attendance data integrity. Not only does the system identify educational staff with high accuracy, but it also provides prompt responses for efficient attendance logging and verification. Beyond its technical benefits, this study significantly contributes to the development of smarter and more efficient attendance technology. The system plays a crucial role in enhancing the discipline of educational staff at STMIK IKMI Cirebon and streamlining attendance management and evaluation across educational institutions.

Attendance Marking using Face Detection

The demand for automated attendance management systems has significantly increased in educational institutions and corporate environments where manual methods are prone to inefficiencies and errors. This paper presents a real-time, facial recognition-based attendance system developed using Flask, OpenCV, and machine learning techniques. The system employs OpenCV’s Haar Cascade classifier for face detection and the K-Nearest Neighbours (KNN) algorithm for face recognition. During user registration, the system captures 10 images per individual to ensure robust recognition across various facial expressions. The web interface, built using Flask, facilitates user interaction for managing attendance records stored in CSV format. The system achieves an accuracy of 92% under optimal lighting conditions, though performance decreases to 80% in low-light environments. Recognition is completed in approximately 2 seconds, making the system suitable for real-time applications in classrooms or offices. Despite handling real-time image streaming efficiently, challenges such as reduced accuracy when handling obstructions like masks or glasses remain. Future enhancements will explore the integration of deep learning models, such as Convolutional Neural Networks (CNNs), to improve robustness and scalability. This system demonstrates the potential of facial recognition technology for automating attendance tracking, with significant applications in both educational and corporate sectors

APLIKASI PENGENALAN WAJAH UNTUK ABSENSI PADA SMA DUA MEI CIPUTAT MENGGUNAKAN METODE HAAR-CASCADE CLASSIFIER BERBASIS WEBSITE

The attendance application is one important system that must be possessed by a school in order to manage student attendance data. Some schools still use manual methods for student attendance by using signatures or calling them one by one. Manual attendance is considered inefficient because the risk of losing attendance sheets is very easy and parents cannot monitor whether their child is attending school or not. This is directly felt by SMA Dua Mei Ciputat with the presence of students who left home but did not attend school. Therefore, this research aims to assist teachers and parents in monitoring students to be more disciplined in coming to school by building a face recognition application for attendance using the haar-cascade classifier method based on a website. The haar-cascade classifier method has the advantages of computation in face detection and the local binary pattern histogram algorithm which has good accuracy in face recognition. The results of this research show that the haar-cascade classifier method can be used to create a face recognition application for attendance with fairly good accuracy, namely 100% with 5 attempts with recognition of one face, 100% with 5 attempts with recognition of two faces, and 20% with 5 attempts with recognition of three faces. With these results, the application is only capable of recognizing two faces simultaneously in the attendance process.

FIRE DETECTION USING AI

Fires are one of the biggest challenges in the world right now, due to the global warming that the planet is currently suffering from. We all know what fires are and what they are capable of causing great damage, whether to humans, animals, or other forms of life. Fires spreading increasingly around the world due to increasing global warming, it has become imperative to develop an intelligent system that detects fires early, using modern technology. Therefore, we used one of the artificial intelligence techniques, which is machine learning, which is one of the popular methods now. Professionals have done a lot of research, experiments, and coding software to detect fires using machine learning. Image processing is a type of processing in which the input image is transformed into another image as output with certain techniques applied to it. In this concept, we will create a fire detecting device using a USB or system camera and a application and apply the concepts of IoT and Image Processing to get real time fire detection results. When the device is switched on, it continuously monitors the area Infront of camera for fires. This is done by using HAAR Cascade Classifier Algorithm. Once detected the system could be hooked up with either fire extinguishers to make them work independently or it could just set an alarm or send a notification to the users mobile device via GSM. The after processing possibilities are endless.

Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system.

Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)

Face recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail.

Face Detection and Recognition for Criminal Identification System

This project showcases a powerful Face Detection and Recognition system intended to improve law enforcement skills by speeding up the identification of suspects in criminal cases. The system uses scalable databases and real-time computation, however instead of using traditional web sources for face detection. Accuracy and dependability are ensured by using the LBPH algorithm for face recognition and the Haar-cascade classifier for feature detection. The smtplib library allows the system to send a Gmail notification when it finds a matching face. The system that has been built contributes to public safety initiatives by improving law enforcement operations and demonstrating versatility for different face detection circumstances. The outcomes show how successfully the system can detect faces in real-time circumstances. The system achieves great performance by creating dynamic datasets and integrating algorithms. Integrating Gmail improves law enforcement response by facilitating prompt contact of identified suspects. This study highlights the role that surveillance technology plays in public safety and crime prevention, and it provides law enforcement authorities with useful solutions. Furthermore, the uses of this facial recognition and detection technology go beyond police enforcement. It can expedite attendance tracking and improve security in access control systems. Because of its adaptability, it can be used in a variety of industries and is expected to have a significant impact.

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

The algorithm with an E-verification method is proposed that combines automated facial recognition technology with digital media and later it combines aspects of secret data exchange and visual cryptography. A face detection technique based on Internet of Things (IoT) selects features using the Principal Component Analysis (PCA) algorithm and the Haar cascade classifier. The selected face serving as a cover follows two level Discrete Haar Wavelet Transform. The shares generated from digital signature and fingerprint are diffused into the converted coefficients. Furthermore, the imperceptibility of the additional noise is increased by a bit-level noise reduction technique. Authenticity is confirmed by regenerating a message digest at the receiving end, and the extraction process operates in complete blindness. The approach is suitable for smart card design and may be used as an automatic recognition system in a real-world setting. Performance comparisons show a notable improvement over other approaches that are comparable. Additionally, the technique is effective against some related attacks.

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

HUMAN FOLLOWING CART

The development of a human-following robot utilizing Raspberry Pi and camera technology represents a significant advancement in autonomous robotics, offering practical solutions for various industries. This project integrates a Raspberry Pi microcomputer, a camera, and ultrasonic sensors to create a robot capable of tracking and following a human operator. The system employs computer vision algorithms, facilitated by a Haar Cascade Classifier, to detect faces in real-time video feeds. The video feed is processed to determine the position of the human within the frame, enabling the robot to navigate accordingly. Ultrasonic sensors are used to measure the distance between the robot and the human, ensuring that a safe and optimal following distance is maintained. The motors and motor drivers are controlled based on the visual and distance data, allowing the robot to move forward, backward, or turn as needed. This combination of technologies enhances worker safety by reducing physical strain and improves operational efficiency by automating the transportation of goods, tools, and supplies. The implementation of this system demonstrates the practical application of affordable and accessible technologies like the Raspberry Pi in creating intelligent, autonomous robots. The project highlights the potential for such robots to revolutionize workflows in warehouses, manufacturing plants, and hospitals, making advanced automation more attainable for a wide range of businesses. The integration of computer vision and sensor-based navigation in human-following robots marks a significant step forward in the evolution of intelligent workspaces and autonomous systems.

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

A Real Time Facial Recognition and Tracking System for Personnel Presence

Abstract: Our project aims to revolutionize the conventional attendance system by implementing a facial recognition-based solution. The existing manual methods are prone to human error and require significant maintenance. By leveraging facial recognition technology, our system will offer improved precision and efficiency, reducing the need for manual work. The system will maintain a database of students' images, matching them during class to mark attendance accurately. Utilizing machine learning techniques, specially the Haar-Cascade classifier and local binary pattern histogram method for face detection and recognition, respectively, our system will ensure reliable attendance tracking. The attendance data will be stored in a MySQL database and Microsoft Excel file, providing a streamlined and secure alternative to the traditional attendance process.

Face Recognition Attendance System

Attendance taking is one of a tedious and important tasks in colleges, universities, organizations, schools, and offices, which 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 main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. 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, sec, and photographs, is trained. The images are extracted using Open CV. 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. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. 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.

Python Based Stress Detection System Using Machine Learning & Algorithm’s

In today's high-pressure world, the need for innovative, non-intrusive stress monitoring solutions is more critical than ever. This paper introduces a sophisticated stress detection system that combines facial expression analysis with machine learning to deliver real-time, continuous stress assessment. Our system employs Haar Cascade classifiers for precise face detection, CNN for feature extraction and utilizes the K-Nearest Neighbors (KNN) algorithm to classify stress-related facial expressions. A significant advantage of our system is its non-intrusive nature, relying solely on visual data, thus eliminating the need for cumbersome wearable sensors. This enhances user comfort and convenience. This paper outlines the design, implementation, and evaluation of our stress detection system, emphasizing its potential applications in workplace wellness, mental health monitoring, and human-computer interaction Key Words: - KNN, Facial Expressions, Machine Learning, Stress Detection, Image Processing, CNN

EYE-BLINK DETECTION ASSISTIVE SYSTEM FOR PARALYZED PATIENT

Paralysis is defined as the complete loss of muscle function in any part of the body. It occurs when there is a problem with the passage of messages between the muscles and the brain. Some paralyzed people cannot move even a single part of the body other than their eyes. Hence, the main aim of this project is to design a real time interactive system that can assist the paralyzed to control appliances such as lights, fans or by playing pre-recorded audio messages, through a predefined number of eye blinks. Image processing techniques have been implemented in order to detect the eye blinks. In our system, the face tracking is accomplished by using a set of trained Haar cascade classifier, and a template matching technique is employed to track the eye. Initially, the involuntary blinks of the paralyzed person are used to locate the patients eyes by finding the number of connected components in a frame.

FACIAL EMOTION RECOGNITION SYSTEM

Face Emotion recognition play a significance role in fields like aid, border management, police work, banking services, and client product. Facial expressions is wide utilized in social communication since they convey heaps of knowledge regarding folks, like moods, emotions, and alternative things. during this paper, we tend to review facial feeling recognition victimisation CNNs and highlight totally different algorithms and their performance impact. Further, we tend to demonstrate that utilizing CNNs during this field - ends up in a considerable performance increase. By forming associate ensemble of recent deep CNNs, we tend to get a FER2013 take a look at accuracy of 91.2%, outperforming previous works while not requiring auxiliary coaching knowledge or face registration. Key Words: Facial Expression, Confusion Matrix, Emotion Optimizer, Haarcascade classifier

Human Emotion Detection Using CNN and Transfer Learning

Facial emotion detection is a critical component in human-computer interaction, mental health assessment, and security systems. In this project, we propose a robust facial emotion detection system leveraging state-of-the-art deep learning techniques. Our system utilizes Convolutional Neural Networks (CNNs) to extract meaningful features from facial images and classify them into five distinct emotional categories: neutral, surprise, sad, happy, and angry. We conducted extensive experiments on a diverse dataset consisting of over 10,000 annotated facial images collected from various sources. Through data augmentation techniques such as rotation, translation, and flipping, we expanded the dataset to enhance model training. Additionally, we employed transfer learning by fine-tuning a pre-trained CNN model, ResNet50, on our dataset to leverage its learned features. This project presents a system for real-time emotion monitoring using computer vision techniques. The system utilizes the Haar Cascade Classifier for face detection in live webcam video streams. Furthermore, we evaluated the system's performance across different lighting conditions, poses, and facial occlusions to assess its robustness in real-world scenarios. Our results indicate that the system maintains consistent performance across diverse conditions, making it suitable for deployment in applications requiring real-time facial emotion recognition.

Virtual Trailroom using Machine Learning

The virtual trail room system receives a real-time video feed from a camera and processes data with the OpenCV computer vision library and Haarcascade classifier. In order to correctly identify faces in the video feed, the Haarcascade classifier is trained on a sizable dataset of human faces.The virtual room system may function independently, which eliminates the need for additional employees and associated expenditures, in contrast to typical security systems that demand the presence of physical security officers.