Cascade Classifier Research Articles

Faculty presence detection and alert system

The exponential growth of inspection systems has increased the demand for efficient and cost-effective solutions. This project introduces a faculty verification system that uses the webcam of the system and OpenCV for real-time facial recognition. The system integrates a Flask-based web interface to provide an intuitive and dynamic user experience. The key features are live detection and system-level video capture by using the Haar Cascade classifier. It gives more importance to accessibility and user-friendliness. Unlike some solutions that work on an external camera or even a Raspberry Pi module, this system works on solely built-in resources. Therefore, the value is guaranteed. The study evaluates performance in face recognition under varying conditions. Focus has been laid on accuracy, responsiveness, and scalability.

Drone-Based Adaptive Hybrid Techniques for Improving Face Detection and Recognition

With the rapid development of Unmanned Aerial Vehicle (UAV) related technologies, Drones gained the opportunity to search locations that are dangerous or difficult for humans to reach. In this regard, Drones technology has been widely used to detect and recognize humans on the ground. In order to identify their potential role in counter-terrorism operations, the aim of this research study is to design and develop a reliable aerial system capable of identifying people with a high detection rate. Furthermore, it presents a solution to all challenges that degrade the resolution when query images are taken from high altitudes, different angles, and long distances. In the context of improving the accuracy of facial detection and recognition, two adaptive techniques are proposed: The first approach is to adaptively adjust the Drone’s altitude, speed, and attitude based on two parameters: Ground sampling distance (GSD), and confidence level of the recognized face. The second approach is to adaptively adjust the resolution of images captured by drone based on the size of the detected facial area. Face detection and recognition are done using the Haar Cascade classifier and Local Binary Pattern Histogram (LBPH) algorithm. The entire system was developed, implemented, and tested using a Hexacopter Drone and onboard vision system. The testing results corroborate the system's practicality and demonstrate that the prototype may be simply implemented to track dangerous people with criminal records.

Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State.

Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera to monitor drivers' facial expressions and detect fatigue indicators such as yawning and eye states. The system is built on a strong architecture and was trained using a diversified dataset under varying lighting circumstances and facial angles. It uses Haar cascade classifiers for facial area extraction and advanced image processing algorithms for fatigue diagnosis. The results demonstrate that the system obtained a 96.54% testing accuracy, demonstrating the efficiency of using behavioural indicators such as yawning frequency and eye state detection to improve performance. The findings show that CNN-based architectures can address major public safety concerns, such as minimizing accidents caused by drowsy driving. This study not only emphasizes the need of deep learning in establishing dependable and practical driver monitoring systems, but it also lays the groundwork for future improvements, such as the incorporation of new behavioural and physiological measurements. The suggested solution is a big step towards increasing road safety and reducing the risks associated with driver weariness.

HOME AUTOMATION SYSTEM USING COMPUTER VISION TECHNIQUE

Home automation using computer vision technology is an emerging field that aims to provide seamless and intelligent control of various home appliances and systems. This technology leverages the power of computer vision algorithms to identify and track objects, gestures, and movements, and then use this information to automate various tasks in the home environment. By integrating computer vision technology with home automation systems, homeowners can enjoy the benefits of a more intuitive and personalized home environment. Some of the potential applications of this technology include smart lighting, climate control, security, and entertainment systems. In this abstract, we will explore the key components of home automation using computer vision technology, the benefits and challenges associated with this technology, and some of the promising applications that are currently being developed. In this project we are going to work on home automation using computer vision technique. Normally home automation is controlled by using wireless devices, mobile phone and voice commands which indicated that a user is involved whil0.e accessing devices. In our project we are using facial detection system to check for the presence of human in a particular are and making the devices to operate without and command form the user. Key Words: Computer Vision, Haar Cascade Classifier, Face detection

Driver inattention detection system using multi-task cascaded convolutional networks

Driver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system.

Метод класифікації МРТ зображень серця за каскадними моделями глибокого навчання

Cardiac MRI is a key method for diagnosing cardiovascular diseases, offering detailed insights into heart structure and function. However, the complexity of cardiac pathologies requires advanced methods for accurate diagnosis. This paper proposes an improved method for classifying heart diseases using a cascade of binary classifiers based on deep learning. By leveraging this cascade approach, the system is able to break down the classification process into multiple stages, each focusing on a specific disease, which enhances the overall accuracy and reliability of the diagnosis. The proposed model is designed to accurately identify a range of heart diseases, including hypertrophic cardiomyopathy, myocardial infarction, dilated cardiomyopathy, and right ventricular abnormalities. The use of a cascade of classifiers enables a more efficient classification process by dividing the task into smaller, manageable subtasks. Each classifier in the cascade is specialized in detecting a particular pathology, allowing the model to focus on the unique characteristics of each disease. This approach reduces the risk of misclassification between similar conditions and improves the overall accuracy of the model. The method achieves an impressive average accuracy of 97.2%, which surpasses the results of known approaches. In particular, individual classifiers demonstrate up to 100% accuracy in detecting hypertrophic cardiomyopathy and right ventricular abnormalities, showcasing the precision of the model in these areas. For myocardial infarction and dilated cardiomyopathy, the method achieves an accuracy of 90%, which, although slightly lower, still represents a high level of diagnostic performance. These results highlight the significant potential of this method for clinical application, offering a more reliable tool for the diagnosis of complex heart conditions. However, the findings also emphasize the necessity of further development, particularly in cases involving less typical or more challenging pathologies. Future work will focus on refining the model and expanding its applicability across a broader range of clinical scenarios to ensure its robustness and effectiveness in real-world settings.

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaque rupture. Manual classification is time-consuming and labor-intensive. Several machine learning-based classification approaches have been proposed and evaluated in recent years. In the current study, we develop a novel pipeline composed of serial classifiers for distinguishing IVUS images into five categories: normal, calcified plaque, attenuated plaque, fibrous plaque, and echolucent plaque. The cascades comprise densely connected classification models and machine learning classifiers at different stages. Over 100,000 IVUS frames of five different lesion types were collected and labeled from 471 patients for model training and evaluation. The overall accuracy of the proposed classifier is 0.877, indicating that the proposed framework has the capacity to identify the nature and category of coronary plaques in IVUS images. Further, it may provide real-time assistance on plaque identification and facilitate clinical decision-making in routine practice.

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

Computer vision-based cascade detection of peeling and cracking in masonry house walls

Self-built masonry structures are commonly used as houses in rural areas of the central plains of China. These structures pose significant safety risks due to the low level of self-building and non-standard designs. The primary type of damage to the walls of masonry houses is cracks, while the decorative layers of the walls may also experience peeling or cracking. The shapes, sizes, and surface characteristics of these damages are complex and unique to each type. To achieve fast identification of surface damage on masonry house walls, this paper proposes a cascade detection model that combines a multilevel cascade classifier with a parallel sub-segmentation network. The VGG16 neural network is used as the backbone for a multi-level series classifier. The model is trained by using a damage image set of the wall decorative layers in rural masonry houses. The classification of background and damage, as well as the classification of cracks and peels, are sequentially completed. Then, the parallel sub-segmentation model uses EfficientNet-B7 as the encoder and combines it with the U-Net framework skeleton to perform pixel-level segmentation of peeling and cracks. Finally, the output of parallel sub-segmentation networks is superimposed and fused to generate a complete image containing segmentation information of peeling and cracks. The cascade form network structure adopted in this model significantly reduces the training difficulty and enhances the model accuracy. Compared with Segformer and Deeplabv3+ network, the average IoU of the proposed method for on-site masonry wall images can be increased by 0.29 and 0.08, respectively.

Investigation and Application of Various Algorithms used in Object Detection and Classification in Image Data

In this article, we compare and analyze the YOLO (You Only Look Once) method, widely employed for object detection in digital image processing, with the Haar feature-based cascade classifier method implemented using the OpenCV library. YOLO, a deep learning–based approach, excels in real-time object detection and recognition applications. In contrast, the Haar method utilizes a traditional approach to rapidly identify features. However, significant performance differences exist between the two methods. Experimental results and performance analyses demonstrate that YOLO provides high accuracy rates and real-time processing speeds in object detection tasks. The code implementations presented in this study will be valuable to researchers new to digital image processing. Additionally, YOLO has shown high performance on large and complex datasets by leveraging GPU capabilities. Experiments with various YOLO versions (e.g., YOLOv4, YOLOv5, YOLOv7) have established it as one of the most suitable options for real-time applications, particularly due to its low latency and high accuracy.

Classification of the difficulty of a climbing route using the transformation of representation spaces and cascading classifier ensemble (CCE)

Climbing has gained popularity in recent years and encompasses various disciplines, among which bouldering stands out as one of the most well-known. Determining the difficulty of a bouldering route is a challenging task due to the varying combinations of handholds. Accurate assessment of the difficulty of a route is important, as it allows a climber’s ability to be measured against that of other climbers. Furthermore, climbers must gradually progress from less to more difficult routes to improve their skills effectively. The difficulty rating of a route poses a complex problem, and previous research has explored machine learning techniques, including deep learning, to address it. We propose a novel approach based on a series of transformations in the representation space that incorporates expert knowledge to improve the classification of climbing route difficulty. We introduce a cascade classifier ensemble (CCE) model specifically designed for levelling classification. Compared to similar works, this model also incorporates explainable artificial intelligence (XAI) mechanisms. With the CEE model, the relevance of specific handholds in the transition from one level of difficulty to the next is obtained. Finally, we illustrate how our proposed model not only achieves good results but also empowers climbers to identify the most crucial sections in each level of difficulty.

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.

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

Machine-learning classification with additivity and diverse multifractal pathways in multiplicativity

Evidence of multifractal structures has spread to a wider set of physiological time series supporting the intricate interplay of biological and psychological functioning. These dynamics manifest as random multiplicative cascades, embodying nonlinear relationships characterized by recurring division, branching, and aggregation processes implicating noise across successive generations. This investigation focuses on how well the diversity of multifractal properties can be specific to the type of cascade relationship between generation (i.e., multiplicative, additive, or a mixture) as well as to the type of noise (i.e., including additive white Gaussian noise, fractional Gaussian noise, and various amalgamations) among 15 distinct types of binomial cascade processes. Cross-correlation analysis of multifractal spectral features confirms that these features capture nuanced aspects of cascading processes with minimal redundancy. Principal component analysis using 13 distinct multifractal spectral features shows that different cascade processes can manifest multifractal evidence of nonlinearity for distinct reasons. This transparency of multifractal spectral features to underlying cascade dynamics becomes less amenable to machine-learning strategies. Fully connected neural networks struggled to classify the 15 distinct types of cascade processes based on the respective multifractal spectral features (45.5% accuracy) yet demonstrated improved accuracy when addressing single categories of cross-generation relationships, that is, additive (91.6%), multiplicative (75.4%), or additomultiplicative (70.6%). While traditional principal component analysis reveals distinct loadings attributed to individual noise processes, multiplicative relationships between generations effectively make the constituent noise processes less discernible to neural networks. Neural networks may lack sufficient hierarchical depth required to effectively distinguish among nonadditive cascading processes, recommending either elaborating multifractal geometry or using alternate architectures for machine-learning classification of cascades with multiplicative relationships. Published by the American Physical Society 2024

Real-Time Human Profiling: Unveiling Age, Gender and Emotions Using Deep Learning

This paper introduces the development of a real-time system, which deploys an integrated use of Flask, deep learning, Convolutional Neural Networks (CNNs) and cascade classifier approach to detect age, gender, and emotion from facial images. While its numerous applications—ranging from marketing and medical services to security surveillance—immediately catches the eye, the facial recognition technology is fast becoming the hot topic. Our suggested system aims at absolutely determining the age, gender or emotional state of a person instantaneously in real life. Flask, Python micro web framework, is at the basis of the software, providing the desired functionalities for the exchange of information between the processing engine and the front end. Convolutional Neural Networks (CNNs) which are a part of deep learning for tasks involving feature extraction and classification are the main tool used by most learning algorithms. CNNs are widely used in face recognition systems mainly due to the fact that they perform very well at such tasks as image processing. On the one hand, the model uses cascade classifiers for superior face detection, thereby finding and separating face regions in input images or video streams. Unlike some of the approaches that require heavy computation, these classifiers are computationally light solutions that can run in real-time even on resource- limited devices. The system capacity to identify age, gender, and emotion accurately in real-time is exemplified through: performance evaluation. We have used various means to stress the system and ensure that it is precise and offers timely results over the given period. By means of varied testing stages, we have discovered that the system usually brings high levels of precision and validity for the numerous data sets and trial situations. In this regard, be it differentiating between individuals’ age or readily identifying the exact gender or recognizing nuanced emotional clues, the system performs at the optimum level.

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.