Local Binary Pattern Histogram Research Articles

Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.

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.

Alzheimer's Patient Support System Based on IoT and ML

Alzheimer's disease poses significant challenges as it progressively erodes memory and identity, severely impacting daily functioning. Patients often experience disorientation, wandering, and are at risk of falls, leading to heightened concerns for caregivers. These difficulties can result in a loss of independence and increased caregiver burden. In response to these challenges, this study introduces an innovative assistive system designed to enhance the safety and quality of life for Alzheimer's patients. The system comprises of two main components: a smart arm band and a facial recognition system. The smart arm band is equipped with a suite of sensors including GPS, accelerometer, and heart rate sensor. These sensors enable real-time monitoring of the patient's location, movement, and physiological parameters. By leveraging these data streams, caregivers can track the patient's activities, detect falls or emergencies, and provide timely assistance when needed. The facial recognition system employs state-of-the-art machine learning techniques, specifically the CAFFE and Local Binary Patterns Histograms (LBPH), to recognize familiar faces in the patient's environment. This capability promotes social interaction and enhances the patient's sense of familiarity and security. Through rigorous testing, the facial recognition system achieves an impressive accuracy of 97% with a low error rate of 3%, validating its effectiveness in real-world scenarios. Overall, the integrative assistive system presented in this study offers a promising solution to address the multifaceted challenges associated with Alzheimer's disease. This system provides caregivers with invaluable support in ensuring the safety and well-being of Alzheimer's patients while fostering social engagement and autonomy.

Deep recurrent neural networks distributed on a Hadoop/Spark cluster for fall detection

Falls detection approaches struggle with both Big Data scalability and upholding individual privacy, this research work proposed a novel approach for posture recognition followed by fall detection, taking advantage of the synergy between Random Forests and Uniform Local Binary Patterns (uLBP) histograms for an accurate and fast posture identification while respecting privacy. Additionally, it referred to deep recurrent neural networks distributed on a Hadoop and Spark platform for time series analysis in fall detection. This combination of methods allowed us to achieve acceptable real-time monitoring precision. This study, therefore addressed two objectives simultaneously: efficiency and scalability in posture recognition using Random Forests and uLBP, and fall detection relying on the recurrent neural network (RNN) for time series processing. The suggested solution is designed for home telemonitoring, where scalability and effective data management are supported through Hadoop/Spark. The integration of these technologies promotes reliable detection without any privacy violation, paving the way for a wider adoption of home monitoring systems for an increasing population of dependent individuals.

Automated Facial Authentication Attendance System

Abstract: This research paper introduces a novel approach to automate attendance tracking in educational institutions through the implementation of a Face Recognition-based attendance system using Python. Traditionally, attendance management has relied on manual processes, prone to errors and time-consuming activities such as roll-call or name calling. The primary objective of this project is to revolutionize attendance management by developing an automated system that utilizes facial recognition technology. By leveraging modern advancements in computer vision, this system aims to streamline the attendancetaking process, enhancing efficiency and accuracy while reducing administrative burdens.Implemented within the classroom environment, the system captures student information including name, roll number,admission number, class, department, and photographs for training purposes. Utilizing OpenCV for image extraction and processing.The workflow involves initial face detection using a Haarcascade classifier, followed by facial recognition utilizing the LBPH (Local Binary Pattern Histogram) Algorithm. Upon recognition, the system cross-references the captured data with an established dataset to automatically mark attendance. Furthermore, to facilitate easy record-keeping, an Excel sheet is dynamically generated and updated at regular intervals with attendance information, ensuring seamless integration with existing administrative processes. This research provides a practical solution for attendance management and also helps in broader discourse on leveraging emerging technologies for optimizing educational and organizational workflows

Adaptive Threshold and Weighted Frequency Domain Histogram of Local Binary Patterns

Wire ropes are crucial load-bearing components in mining conveyance equipment, and machine vision is one of the methods used to assess the surface damage condition of wire ropes. In response to the light-sensitive nature of local binary patterns, which leads to issues such as differing feature values for similar textures and susceptibility to the influence of excessively large or small pixels within local windows, hindering the accurate reflection of window structure information and exacerbating the introduction of considerable feature noise, an investigation is conducted. To enhance the gradient structural information among pixels within local pixel window, an adaptive threshold binary pattern feature operator is proposed. This operator utilizes the mean and variance within the local window to balance the central pixel value, thereby enhancing the interconnection among neighboring pixels. To perform feature selection on block histograms, a block-weighted approach is employed. This approach utilizes the concept of block weighting and employs correlation coefficients to preprocess feature vectors, thereby enhancing classification accuracy. The algorithm experiments were conducted on a dataset of mine wire ropes. The results indicate that the improved local binary pattern significantly enhances the classification accuracy of the wire rope dataset, achieving an accuracy of 97.3%.

Advancing Attendance: A Facial Recognition System Empowered by Deep Learning Techniques

Many studies during the last decade tried to provide an automatic technique for facial recognition and identification challenge especially in security systems. In this study, we suggested two methods for student attendance problem based on image processing and machine learning algorithms. The first method uses Haar cascade classifier with the Local Binary Patterns Histograms (LBPH) model and the second method composed from Histograms of Oriented Gradient (HoG) followed by the Convolutional Neural Network (CNN) model. Both methods take a collection of random student images taken from low quality sources as input. A set of image processing filters are first applied on images to enhance the method of extracting the face boundary. Then, each model will be trained using random images from student dataset. The trained model is tested using testing set. The results showed that the method that employ CNN model with HoG provides high accuracy value of 98.44%. While, the accuracy of LBPH model with Haar Cascade classifier is 95.63%.

FACIAL RECOGNITION ATTENDANCE SYSTEM

Colleges have historically faced a great deal of difficulty with student attendance, necessitating a large time and effort investment from staff in manual tracking. Even though they are in place, the existing biometric attendance systems are not entirely automated, which causes delays in processing fingerprints, maintenance issues, and inefficiencies in time. Given that almost everyone has a smartphone and is continuously online in this day and age, a more simplified method is necessary. This study suggests using sophisticated object identification algorithms to check attendance using faculty members' smartphones. Because of its effectiveness in face detection and the addition of Microsoft Azure's face API for database recognition, YOLO V3 (You Only Look Once) is the preferred option among these. One special feature of the system is that it takes pictures of the classroom at the start and finish of every class to make sure everyone is present. After determining the number of students in each photograph, YOLO V3 separates the faces that are known and those that are unknown, creating distinct spreadsheets. Monthly email reminders are also sent to teachers, parents, and students. The system that has been put into place shows strong real-time performance in counting and detecting jobs, with excellent facial recognition accuracy and overall efficiency. Keywords:, OpenCV, Local Binary Pattern Histogram (LBPH), Real-time Tracking, Facial Analysis, You Only Look Once (YOLO V3), Firebase Database.

Face Recognition Using LBPH and CNN

Objective:: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods:: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results:: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion:: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

Streamlined Workforce Management System Using AI

In today's digital landscape, face recognition technology has emerged as a versatile and non-invasive tool used across sectors, despite its lower accuracy compared to iris or fingerprint recognition. Its applications range from security and authentication to optimizing attendance systems in educational institutions and workplaces. This paper introduces an innovative class attendance system employing face recognition technology, replacing manual processes prone to inaccuracies like proxy attendance. The system involves database creation from student images, utilizing Haar-Cascade classifiers for face detection, and Local Binary Pattern Histogram algorithms for live recognition from classroom video streams. It automates attendance data distribution to faculty members, enhancing efficiency and precision. Simultaneously, the security industry necessitates alert and vigilant security personnel to prevent breaches. A sophisticated hybrid system, leveraging machine learning techniques like OpenCV and EAR/MAR algorithms alongside UHD cameras, detects guard drowsiness and alerts them promptly using sound cues and preventive measures, ensuring continual vigilance. This innovative hybrid system fortifies security guard surveillance, mitigating potential vulnerabilities due to lapses in attentiveness. Key Word: Face Recognition Technology, Biometrics, Attendance Management, Security, Authentication, Surveillance, Machine Learning, Open CV, Security Guard Monitoring, Vigilance, Drowsiness Detection.

FACIAL EXPRESSION EMOTION RECOGNITION USING LOCAL BINARY PATTERNS HISTOGRAMS

Local Binary Patterns (LBP) histograms are used as texture descriptors by the proposed facial expression emotion detection system to identify various face expressions in photos. Localized facial patterns are represented by histograms formed by the extraction of LBP features based on pixel intensity comparisons after pre-processing operations such as picture normalization and scaling. Using a labeled emotion dataset, these histograms are used as feature vectors to train a machine learning model. The model's performance is evaluated using evaluation measures, such as accuracy, precision, recall, and F1 score. The system, which was developed with OpenCV and scikit-learn, shows a reliable and useful method for recognizing facial expressions that can be used in practical settings. Key Words: Facial Recognition, Deep Learning, Image Recognition

COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

BackgroundCOVID-19 is a disease that caused a contagious respiratory ailment that killed and infected hundreds of millions. It is necessary to develop a computer-based tool that is fast, precise, and inexpensive to detect COVID-19 efficiently. Recent studies revealed that machine learning and deep learning models accurately detect COVID-19 using chest X-ray (CXR) images. However, they exhibit notable limitations, such as a large amount of data to train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), and longer run-time.ResultsIn this study, we proposed a new approach to address some of the above-mentioned limitations. The proposed model involves the following steps: First, we use contrast limited adaptive histogram equalization (CLAHE) to enhance the contrast of CXR images. The resulting images are converted from CLAHE to YCrCb color space. We estimate reflectance from chrominance using the Illumination–Reflectance model. Finally, we use a normalized local binary patterns histogram generated from reflectance (Cr) and YCb as the classification feature vector. Decision tree, Naive Bayes, support vector machine, K-nearest neighbor, and logistic regression were used as the classification algorithms. The performance evaluation on the test set indicates that the proposed approach is superior, with accuracy rates of 99.01%, 100%, and 98.46% across three different datasets, respectively. Naive Bayes, a probabilistic machine learning algorithm, emerged as the most resilient.ConclusionOur proposed method uses fewer handcrafted features, affordable computational resources, and less runtime than existing state-of-the-art approaches. Emerging nations where radiologists are in short supply can adopt this prototype. We made both coding materials and datasets accessible to the general public for further improvement. Check the manuscript’s availability of the data and materials under the declaration section for access.

Sistem Pengenalan Wajah pada Sistem KYC dengan Algoritma Local Binary Pattern Histogram

With the increasing internet usage post-pandemic, ensuring the security of a fintech application becomes imperative. Bangbeli implements KYC procedures using facial recognition technology and stringent security protocols to verify identities and safeguard users' personal data in compliance with Bank Indonesia regulations. Utilizing Haar Cascade Classifier, Local Binary Pattern Histogram, and histogram equalization, an API (Application Programming Interface) has been created for facial training and prediction. These methods were chosen for their credibility, achieving an 88% accuracy with 33 samples and 90% with 10 samples. This study focuses on constructing an API for mobile services at Bangbeli, achieving 87.5% accuracy, 81.25% precision, 87.5% recall, and a 25% error rate. The model demonstrates good performance in facial recognition, with an acceptable error rate. Although precision is slightly lower than recall, it suggests the model is more inclined to identify most positive data with some errors rather than discard potentially identifiable faces.

Face Recognition Based Attendance Software and Reporting using OpenCV

The face is the identity of a person. The methods to exploit this physical feature have seen a great change since the advent of image processing techniques. The attendance is taken in every school, colleges and libraries. Traditional approach for attendance is professor calls student name and record attendance. It takes some time to record attendance. For each lecture this is a waste of time. To avoid these losses, we're going to use automatic process which is based on image processing. Identification of any people in any organization or colleges for the purpose of attendance marking is one of such software. Authentication is a significant issue in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications. The use of Attendance Management System is to perform the regular activities of attendance marking and analysis with less human intervention. In this new approach, we are using face detection and face recognition system. This face detection distinguishes faces from non-faces and is essential for accurate attendance. The other strategy is facing recognition for marking the student’s attendance. We have used OpenCV for this system. The camera will be connected using OpenCV module that converts the image into the RGB format which in turn is mapped to the pre-trained neural networks using HOG algorithm to recognize the face pixels. This system offers effective way to manage the attendance system which is time efficient and offers massive scaling for future purposes. 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.

Smart Face Attendance System

Smart Face Attendance System

Enhancing Attendance Management through Real-Time Face Recognition using combination of LBP Histogram and Linear Discriminant Analysis

Face recognition technology's excellent accuracy and non-intrusiveness have made it a major player in the attendance management space. This research study presents an advanced method of tracking attendance through the integration of a Face Recognition System (FRS) with Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) histogram analysis. The architecture of the suggested attendance system is described in the study, with an emphasis on how LDA and LBP techniques are used to increase the accuracy of face feature extraction and recognition. Through the use of LDA, the system improves face data's discriminative strength, producing attendance records that are more trustworthy. Robust facial texture and pattern identification are simultaneously made possible by LBP histogram analysis, particularly in a variety of environmental settings. To sum up, this study advances biometric attendance systems by demonstrating how LDA and LBP histogram analysis may improve the precision and dependability of face recognition systems used for attendance tracking

RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK SISTEM PRESENSI DI PRODI TEKNIK INFORMATIKA UNIVERSITAS ISLAM RADEN RAHMAT MALANG MENGGUNAKAN METODE LBPH DAN LIBRARY OPENCV BERBASIS ANDROID

The attendance system is a mechanism to record the attendance of students in each class they take. The attendance system serves as evidence of students' presence in the learning process. In its journey, Universitas Islam Raden Rahmat has been using manual attendance through attendance journals in the learning process. However, with technological advancements, the attendance process has followed this trend, one of which is face recognition. In this research, the researcher designed a face recognition application for the attendance system that can be implemented on mobile platforms, especially Android. This application using the Local Binary Pattern Histogram (LBPH) method and the OpenCV library. The objective of this application is to enhance the flexibility and efficiency of the current attendance system by eliminating the need to repeatedly retrieve attendance forms, preventing fraud, and reducing paper usage. By using the waterfall system development method, testing showed a face recognition accuracy of 70%.

Neural Network Classification in Javanese Handwriting Recognition using Projection Profile Histogram and Local Binary Pattern Histogram

Indonesia consists of various regional tribes, where each tribe has cultural diversity and some even have their own regional letters, like Javanese tribe has Javanese characters. Javanese letters consist of 20 basic letters called Nglegena script. Subject about Javanese language is delivered to elementary student until now aims to preserve Indonesian culture especially the Javanese. In this study, we present two feature extraction methods are Local Binary Pattern (LBP) and Profile Projection (PP). Neural Network (NN) chosen as classifiers for classifying 20 javanese letters Nglegena. Some digital image processing processes are carried out, are image inversion, dilation, denoising and skeletoning. The Javanese script dataset is taken from the Kaggle database with the name Aksara Jawa: Aksara Jawa Custom Dataset, consists of 2154 train images and 480 test images. The experiment were carried out in two models, Projection Profile Histogram - Neural Network (PPH-NN) and Local Binary Pattern Histogram - Neural Network (LBPH-NN). The experiment show that both feature extraction methods have very good performance, 99.98% PPH-NN and 89.6% LBPH-NN on average.

Prototype of Melon Fruit Quality Sorter Based on Skin Texture Using Local Binary Pattern Histogram

Cucumis Melo L is one of the popular fruits in Indonesia. The numerous benefits and various contents of melon make it highly valuable for human health. Determining the quality of melon is crucial, considering its numerous benefits and contents. The quality of melon is determined by the presence of net-like patterns on the surface of the fruit's skin when it is ripe for harvest. Currently, conventional methods that rely on direct visual observation are still commonly used to sort the quality of melons. Therefore, a prototype system was developed to sort the quality of melons based on the texture of their skin. The purpose of this system is to reduce errors caused by eye fatigue and variations in accuracy during the sorting process. In this research, the feature extraction method of the local binary pattern (LBP) was employed, along with the K-Nearest Neighbor (KNN) method for classification. The classification was divided into two grades: Grade A and Grade B melons. The testing phase involved 200 data samples, with each grade consisting of 100 data samples. The results of the testing phase showed a success rate of 96% for the system. Based on the percentage, it can be concluded that the system has successfully performed well in sorting the quality of melons based on the texture of their skin.

Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.