Binary Pattern Research Articles

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.

LBP-CNN Fusion for Driver Fatigue Detection

Nowadays, individuals tend to rely on their own means of transportation. As the number of vehicles on the road continues to rise, the occurrence of accidents has also increased. Specifically, road accidents are becoming more prevalent, with driver drowsiness being a significant contributing factor. Approximately 2.3 lakh out of the 23 lakh road mishaps that transpire in the country each year are attributed to drowsiness. Recent data indicates that an estimated 2.3 lakh to 3.5 lakh road accidents are a direct consequence of sleepiness[9]. To mitigate the occurrence of such accidents, we propose the implementation of a drowsy detection system for drivers. This system ensures that both the passengers and the driver can embark on their journey with the assurance of safety. The system utilizes a camera to monitor the driver's facial expressions and issues alerts accordingly. It analyzes various facial features such as eye closure, yawning, and signs of fatigue, subsequently notifying the driver to remain attentive. This approach involves the utilization of the local binary pattern texture analysis method in conjunction with convolutional neural networks (CNNs). By doing so, it effectively highlights the textural variations in the driver's face, thereby clearly identifying the relevant facial features. The camera detects the driver's face, captures the image, and subsequently converts it into LBP images. This process enables the system to detect subtle indicators of drowsiness, ultimately aiding in the prevention of accidents. Consequently, this system significantly reduces the occurrence of drowsiness-related accidents, thereby saving numerous lives

REAL TIME FACE ATTENDANCE SYSTEM USING DEEP LEARNING

One's face is a physical depiction of who they are. As a result, we have suggested a facial recognition- based automatic student attendance system. The usage of face recognition technology in everyday life is particularly beneficial for security control systems. Face recognition is used by the FBI (Federal Bureau of Investigation) for criminal investigations as well as the airport security system to identify criminals. In the first step of our suggested method, video framing, the camera is activated using a user-friendly interface. The Viola-Jones method is used to identify and segment the face ROI from the video frame. If necessary, image size scaling is done at the pre-processing step to prevent information loss. After using the median filter to reduce noise, color pictures are transformed into grayscale versions. Then, to improve the contrast of the pictures, contrast-limited adaptive histogram equalization (CLAHE) is applied. Principal component analysis (PCA) and improved local binary pattern (LBP) are used appropriately in the face identification stage to extract the characteristics from facial pictures. By minimizing the lighting impact and boosting identification rates, the improved local binary pattern outperforms the original local binary pattern in our suggested method. The features that were retrieved from the training pictures and the features that were extracted from the test images are then compared. The face pictures are then identified and categorized using the upgraded LBP, PCA, and algorithm that produced the best results. The acknowledged student's attendance will then be recorded and saved in the excel file. A warning will be sent if a student signs in more than once. The student who is not enrolled will also be allowed to register right away. When two photographs per person are trained, the average recognition accuracy is 100% for high-quality images, 94.12% for low-quality images, and 95.76% for the face database.

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

Performance Analysis of Child Emotion Detection using Haar Cascade and CNN

A method for identifying human emotions from facial expressions is called facial emotion detection. This essay focuses on analyzing youngsters with autism's facial expressions to determine their feelings. In this research, five emotions are examined. These feelings include anger, surprise, sadness, happiness, and neutrality. Image processing and machine learning techniques are used to identify the emotions of autistic youngsters. The local binary pattern features are taken from the faces of youngsters with autism. Emotions are categorized using machine learning algorithms. Neural networks and support vector machines are two types of machine learning classifiers used in the classification process. Child age detection in film shots plays a vital role in ensuring compliance with age-restricted content regulations and safeguarding the well-being of underage actors. This abstract presents an overview of recent advancements, methodologies, and applications in using machine learning (ML) for child age detection.

Face Recognition and Identification System for Crime Detection Using Python Programming Language

Abstract: Throughout the years, finding criminals has posed a challenging endeavor. In the past, the method relied on leads derived from evidence discovered at the crime scene. Genetic evidence is comparatively straightforward to trace, yet wrongdoers have grown increasingly proficient at obscuring their trail and leaving behind no discernible mark. To address this issue, a face recognition system for crime detection was developed. In this study, a qualitative method was used to collect the face images of criminals in collaboration with law enforcement agencies. An API was built using the criminals National Identification Numbers. Then, a face recognition view that returns the object of the user gotten from the National Identification Number was built. The returned user objects (including face images of the user) underwent preprocessing steps, including normalization, resizing, and noise removal, to enhance the quality and standardize the facial features. Local Binary Patterns algorithm in Python OpenCV library was applied to the preprocessed face images to extract discriminative features from the preprocessed face images. These features aimed to capture the unique characteristics of each face for subsequent recognition. The algorithm was trained on the extracted features and tested given a new input image. The interface component specification of the system was designed using HTML, CSS, and JavaScript client-side technologies.

A Comprehensive Approach to Brain Tumor Classification in MRI: Unifying Classical Local Binary Patterns and Convolution Neural Networks

Brain tumors, among the most perilous neurological disorders affecting the human nervous system, are categorized into glioma, meningioma, and Pituitary types. The proposed system combines Classical Local Binary Patterns (CLBP), Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) to extract texture information from MRI images. The methodology comprises three key steps: image pre-processing, feature extraction, and classification. By evaluating a publicly accessible brain tumor dataset, the proposed approach attains an impressive accuracy of 96%. The findings highlight the potential uses of the CLBP+CNN approach in clinical diagnosis and treatment planning, demonstrating the possibility of accurate and efficient brain tumor classification. The proposal introduces future extension methods like CLBPs (DLBP, ӨLBP), where the DLBP approach incorporates a 'D' parameter specifying the distance between neighboring pixels. The ӨLBP Method evaluates the pixel value by changing the 'Ө' value, i.e., 150, 450, 900 and 1200. The classification of tumors involved the application of various classification methods, namely ANN (Artificial Neural Networks), AlDE (Ant Lion Optimizer Differential Evolution), and LDA (Linear Discriminant Analysis). This classification is executed using feature extractions derived from CLBP (DLBP & ӨLBP) applied to MRI images within the dataset.

Three 1-bit Speckle-embedded pulse-width modulation patterns for robust absolute 3D measurement

Abstract In 3D shape measurement techniques using structured light, 1-bit pulse-width modulation (PWM) patterns and 1-bit speckle patterns can be projected at high speed. However, when combining PWM and speckle patterns to integrate their advantages, the decoupling problem is insurmountable. In this work, a novel 1-bit speckle-embedded pulse-width-modulation (SP-PWM) patterns was proposed to achieve absolute 3D shape measurement using only three binary patterns. Our method consists of three main steps: First, a sinusoidal pattern reconstruction network (SPRNet) was proposed to eliminate the high-order harmonics and speckle patterns in the SP-PWM patterns and obtain high-quality sinusoidal patterns. Second, a multi-temporal spatial correlation matching algorithm (MTSCMA) was proposed to obtain a coarse disparity map from the three SP-PWM patterns. Third, the high-accuracy wrapped phase map is used as an additional constraint for refining the coarse disparity map to obtain the final high-accuracy disparity map for absolute 3D measurement without phase unwrapping. Our method combines the advantages of fringe projection profilometry techniques for high-precision wrapped phase retrieval and speckle correlation matching algorithms for robust and unambiguous disparity map calculation. The experimental results demonstrated that our method could realize high-precision absolute 3D shape measurement with an accuracy of 0.057mm using only three 1-bit SP-PWM patterns. Furthermore, different simulation noises were used to demonstrate the robustness of the proposed method.

Writer verification using feature selection based on genetic algorithm: A case study on handwritten Bangla dataset

AbstractAuthor verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results.

MEA-EFFormer: Multiscale Efficient Attention with Enhanced Feature Transformer for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.

Enhanced Local Binary Pattern Algorithm for Facial Recognition Using Chinese Remainder Theorem

Current biometrics research focuses on achieving a high authentication success rate for identity management and discussing the threat  of various security attacks. Local Binary Pattern (LBP), one of the methods for feature extraction, and Chicken Swarm Optimization (CSO),  one of the strategies for feature selection, were used for user identification and authentication. LBP requires high computational time to  extract features from the facial images. The Chinese Remainder Theorem (CRT) was used to reduce its computational time by formulating  an Enhanced Local Binary Pattern (ELBP). Michael Olugbenga Banji Abolarinwa (MOBA) database was created specifically for  this study. 600 frontal facial images of 200 people were collected, each with three images. 360 images were used for training while  240 images were used for testing. MATLAB (R2016a) was used to run the simulation. The time it took to classify the facial images when  LBP and CSO were combined and when ELBP and CSO were combined were enumerated. The LBP-CSO achieved a false-positive rate  (FPR) of 11.67%, a sensitivity (SEN) of 92.78%, a specificity (SPEC) of 88.33%, a precision (PREC) of 95.98%, and an accuracy of 91.67% in  119.10 seconds at 0.80 thresholds for face recognition. ELBP-CSO obtained an FPR of 5.00%, SEN of 95.00%, SPEC of 95.00%, PREC of  98.28%, and accuracy of 95.00% in 79.16 seconds. The results showed that LBP-CSO took an average of 119.10 seconds and ELBP-CSO took  an average of 79.16 seconds. In conclusion, the performance of CSO-ELBP justifies the usage of LBP enhancement with CRT. 

The regional disparities in liver disease comorbidity among elderly Chinese based on a health ecological model: the China Health and Retirement Longitudinal Study

PurposeThis study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model.MethodBasic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes.ResultsAmong the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities.ConclusionThe comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Modelling appearance variations in expressive and neutral face image for automatic facial expression recognition

AbstractIn automatic facial expression recognition (AFER) systems, modelling the spatio‐temporal feature information in a specific manner, coalescing, and its effective utilization is challenging. The state‐of‐the‐art studies have examined integrating multiple features to enhance the recognition rate of AFER systems. However, the feature variations between expressive and neutral face images are not fully explored to identify the expression class. The proposed research presents an innovative approach to AFER by modelling appearance variations in both expressive and neutral face images. The prominent contributions of the work are developing a novel and hybrid feature space by integrating the discriminative feature distribution derived from expressive and neutral face images; preserving the highly discriminative latent feature distribution using autoencoders. Local binary pattern (LBP) and histogram of oriented gradients (HOG) are the feature descriptors employed to derive the discriminative texture and shape information, respectively. The component‐based approach is employed, wherein the features are derived from the salient facial regions instead of the whole face. The three‐stage stacked deep convolutional autoencoder (SDCA) and multi‐class support vector machine (MSVM) are employed to address dimensionality reduction and classification, respectively. The efficacy of the proposed model is substantiated by empirical findings, which establish its superiority in terms of accuracy in AFER tasks on widely recognized benchmark datasets.

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%.

Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN

The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these technological strides, there are critical areas that require further exploration and development. In this landscape, computer-aided diagnostic systems and artificial intelligence, particularly deep learning methods like the region proposal network, the dual path network, and local binary patterns, have become pivotal. However, these methods face challenges such as limited interpretability, data variability handling issues, and insufficient generalization. Addressing these challenges is key to enhancing early detection and accurate diagnosis, fundamental for effective treatment planning and improving patient outcomes. This study introduces an advanced approach that combines a Convolutional Neural Network (CNN) with DenseNet, leveraging data fusion and mobile edge computing for lung cancer identification and classification. The integration of data fusion techniques enables the system to amalgamate information from multiple sources, enhancing the robustness and accuracy of the model. Mobile edge computing facilitates faster processing and analysis of CT scan images by bringing computational resources closer to the data source, crucial for real-time applications. The images undergo preprocessing, including resizing and rescaling, to optimize feature extraction. The DenseNet-CNN model, strengthened by data fusion and edge computing capabilities, excels in extracting and learning features from these CT scans, effectively distinguishing between healthy and cancerous lung tissues. The classification categories include Normal, Benign, and Malignant, with the latter further sub-categorized into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In controlled experiments, this approach outperformed existing state-of-the-art methods, achieving an impressive accuracy of 99%. This indicates its potential as a powerful tool in the early detection and classification of lung cancer, a significant advancement in medical imaging and diagnostic technology.

A Robust Approach for Ulcer Classification/Detection in WCE Images

Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.

Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

HIDDEn: Robust Data Hiding for Medical Images with Encryption and Local Binary Pattern

HIDDEn: Robust Data Hiding for Medical Images with Encryption and Local Binary Pattern

A machine vision approach for detecting changes in drill core textures using optical images

Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation.