Local Binary Pattern Algorithm Research Articles

An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm

An efficient facial emotion recognition using convolutional neural network with local sorting binary pattern and whale optimization algorithm

Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluations demonstrate the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes. In the case of the best-implemented scenario involving multiple trials, the detection stage achieved a copy-move accuracy of 97.8 %.

An Image Feature Extraction Algorithm Based on Tissue P System

As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence.

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.

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.

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

Effective Facial Expression Recognition System Using Machine Learning

The co Facial expression recognition (FER) is a topic that has seen a lot of study in computer vision and machine learning. In recent years, deep learning techniques have shown remarkable progress on FER tasks. With this abstract, A Novel Is Advised By Us FER method that combines combined use of k-nearest neighbours and long short-term memory algorithms better efficiency and accurate facial expression recognition. The proposed system features two primary steps—feature extraction and classification—to get results. When extracting features, we extract features from the facial images using the Local Binary Patterns (LBP) algorithm. LBP is a simple yet powerful feature extraction technique that captures texture information from the image. In the classification stage, we use the KNN and LSTM algorithms for facial expression recognition. KNN is a simple and effective classification algorithm that finds the k closest to the given value neighbours to the test training-set-sample and assigning it to the class that is most frequent among its neighbours. However, KNN has limitations in handling temporal information. To address this limitation, we propose to use LSTM, which is a subclass of RNNs capable of capturing temporal relationships in time series data. The LSTM network takes as input the LBP features of a sequence of facial images and processes them through a series of LSTM cells to estimate the ultimate coding of the phrase. We examine the planned and system on two publicly available records: the CK+ and the Oulu-CASIA datasets. According on the experimental findings, the proposed system achieves performance at the cutting edge on both datasets. The proposed system performs better than other state-of-the-art methods, including those that use deep learning systems, quantitatively, in terms of F1-score and precision.In conclusion, the proposed FER system that combines KNN and LSTM algorithms achieves high accuracy and an F1 score in recognising facial expressions from sequences of images. This system can be used in many contexts, including human-computer interaction, emotion detection, and behaviour analysis.

Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition.

Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.

Biometric passport security based on face recognition and biometric encryption using secure force algorithm into the HCC2D code

ABSTRACT This paper proposed a noble face recognition and biometric encryption technique for the biometric passport by applying encrypted biometric data encoded into the High-Capacity Color Two-Dimensional (HCC2D) Code to secure the biometric data. The proposed biometric encryption method is attained by applying the Secure Force (SF) algorithm and encoding it into the HCC2D code. The face recognition technology is achieved by combining the Viola-Jones (VJ) algorithm and the Local Binary Patterns (LBP) algorithm to extract the facial features and generate the template. This method will ensure the safety and security of a biometric passport from unauthorized access without knowing the passport holder. The proposed method will provide more security for border crossing and illegal immigrants for national security.

Machine learning studies for liquid crystal texture recognition

ABSTRACT Machine learning techniques such as local binary pattern algorithm and convolutional neural network were applied to study textures of liquid crystal compound (S)-4’-(1-methyloctyloxycarbonyl) biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]-benzoate (3F7HPhH7). This compound exhibits in its polymorphism several smectic phases (smectic A, ferroelectric smectic C and antiferroelectric smectic C) and two glass states of antiferroelectric smectic C phase, which textures are difficult to distinguish. Proof-of-concept evidence is provided, demonstrating the ability of machine learning algorithms to identify transition temperatures and the respective phases involved. The article describes the procedure for preparing a dataset of textures obtained from polarised microscopy for classification using convolutional neural networks, especially in cases where the image dataset is unbalanced. It utilises the feature of their self-similarity and image augmentation, particularly the cropping procedure. A Kolmogorov–Smirnov test was conducted to check if selected images do not carry the same information.

Monitoring drought induced photosynthetic and fluorescent variations of potatoes by visible and thermal imaging analysis

Accurate determination of photosynthetic parameters is critical to evaluate crop physiological and growth processes. This study aimed to estimate photosynthetic and fluorescence variables of potatoes by visible and thermal imaging fusion analysis. Two-years pot experiments were conducted in a climate chamber with different irrigation treatments. Multi-modal image features of crop canopy were extracted from both visible and thermal images by color component extraction, discrete wavelet transformation, gray level co-occurrence matrix, and local binary pattern algorithms. Extracted features were subsequently employed to build partial least squares regression (PLSR) models for estimation of transpiration rate (Tr), net photosynthetic rate (An), stomatal conductance (GSW), electron transport rate (ETR), and maximum photochemical efficiency under Photosystem II (Fv'/Fm'). Results showed that Mask Region-Convolutional Neural Network (Mask R-CNN) performed satisfactorily on canopy segmentation with intersection over union of 87.29 % and 86.93 % in visible and thermal images, respectively. Three different types of models that either using only visible image features (PLSRRGB), or only thermal image features (PLSRT) or both visible and thermal image features (PLSRRGB+T) as inputs were compared. Results showed that PLSRRGB+T had superior estimation performance in terms of R2 and RMSE. It achieved the highest R2 of 0.85 with An and the lowest R2 of 0.66 with GSW for Zhongshu 5, while it had the highest R2 of 0.86 with Fv'/Fm', and the lowest R2 of 0.71 with Tr and An for D681. This implied the potential of visible and thermal image-driven method for quick and accurate estimation of photosynthetic traits of crops grown in controlled environment.

The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.

Distinguishing the Focal-Conic Fan Texture of Smectic A from the Focal-Conic Fan Texture of Smectic B

This publication presents methods of distinguishing the focal texture of the conical smectic phase A (SmA) and the crystalline smectic B phase (CrB). Most often, characteristic transition bars are observed in polarized light at the temperature point of the SmA–CrB phase transition. TOApy software transforms each image from a series of images recorded during POM observation to a function of light intensity versus temperature. Thermo-optical analysis is a powerful quantitative tool to notice this phase transition, but it has some limitations. The other applied method, the local binary pattern (LBP) algorithm, with high probability, detects differences between the textures of the conical focal fan of the SmA and CrB phases. The LBP algorithm is an efficient tool for texture classification.

Recognition of Ellipsoid-like Herbaceous Tibetan Medicinal Materials Using DenseNet with Attention and ILBP-Encoded Gabor Features.

Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.

Research on Multi-Scale Feature Extraction and Working Condition Classification Algorithm of Lead-Zinc Ore Flotation Foam

To address the problems of difficult online monitoring, low recognition efficiency and the subjectivity of work condition identification in mineral flotation processes, a foam flotation performance state recognition method is developed to improve the issues mentioned above. This method combines multi-dimensional CNN (convolutional neural networks) characteristics and improved LBP (local binary patterns) characteristics. We have divided the foam flotation conditions into six categories. First, the multi-directional and multi-scale selectivity and anisotropy of nonsubsampled shearlet transform (NSST) are used to decompose the flotation foam images at multiple frequency scales, and a multi-channel CNN network is designed to extract static features from the images at different frequencies. Then, the flotation video image sequences are rotated and dynamic features are extracted by the LBP-TOP (local binary patterns from three orthogonal planes), and the CNN-extracted static picture features are fused with the LBP dynamic video features. Finally, classification decisions are made by a PSO-RVFLNs (particle swarm optimization-random vector functional link networks) algorithm to accurately identify the foam flotation performance states. Experimental results show that the detection accuracy of the new method is significantly improved by 4.97% and 6.55%, respectively, compared to the single CNN algorithm and the traditional LBP algorithm, respectively. The accuracy of flotation performance state classification was as high as 95.17%, and the method reduced manual intervention, thus improving production efficiency.

Vote Now Application

The E-voting system offers internet voting. To stop voter fraud, we use two different levels of security. As the first level of security, a web camera records the voter's face and uploads it to the database. After that, the person's face is compared to the face in the database to verify its authenticity. The two faces are compared with the help of the Local Binary Pattern algorithm. The plan's foundation is combining two images and assigning a value to a central pixel. Those central pixels will have values of either 0 or 1. If the value is less than a pixel, a histogram of the labels is calculated and used as a descriptor.

FAKE IMAGE IDENTIFICATION USING MACHINE LEARNING

Biometric techniques are now helpful in identifying a person's identity, but criminals alter their look, behaviour, and psychological makeup to trick identification systems. We are employing a novel method called Deep Texture Features extraction from photos to solve this issue, followed by the construction of a machine learning model using the CNN (Convolution Neural Networks) algorithm. This method is also known as LBPNet or NLBPNet since it largely relies on the LBP (Local Binary Pattern) algorithm for features extraction. LBPNET, a machine learning convolution neural network, is the name of the network we created for this research to identify fraudulent face photographs. Here, we will first extract LBP from the photos before training the convolution neural network on the LBP descriptor images to create a training model. Every time we submit a new test picture, the training model will be applied to that test image to determine if it includes a false image or not. Details regarding LBP are shown below. keywords: Biometry, Identity, Recognition, Detection, Fake face.

Prediction of variable-groove weld penetration using texture features of infrared thermal images and machine learning methods

When workpieces with varying grooves are welded, changes in the groove angle of the base metal can affect the butt weld penetration, thus affecting the final weld forming quality. Therefore, it is particularly important to be able to diagnose variable-groove weld penetration in real time. In this study, a novel variable-groove weld-penetration diagnosis technique using the texture features of infrared thermal images and machine learning methods is proposed. First, an improved local binary pattern algorithm based on the window standard deviation is introduced for texture analysis of welding-temperature field images. Second, the feasibility of using the images' texture features for predicting variable-groove weld penetration is demonstrated. Finally, a predictive machine-learning-based model for predicting variable-groove weld penetration is developed, and the effects of the size and location of the monitored area on the model's performance are quantified. The experimental results suggest that the proposed predictive model is feasible and offers a new and effective method for real-time diagnosis of variable-groove weld penetration. The research results of this paper enrich the application of optical inspection in the field of welding processing.

Facial Recognition System Using Local Binary Pattern (LBP) Algorithm as a Tool in Curbing Agricultural Loan

Face recognition (FR) technology can be used in wide range of applications such as identity authentication, access control, and surveillance. Interests and research activities in face recognition have increased significantly over the past twenty years, in this research, local binary pattern was be used to design a system that will store all the faces of the Central Bank of Nigeria (CBN) anchor borrowers’ loan beneficiaries, the system will use farmers faces binary information and inject the binary information into the recovery farmers database so that no multiple loan will be given to any defaulter and their facial identity will be revealed. The final output of the anchor borrower programme(ABP) is to create economic linkage between smallholder farmers and reputable large-scale processors with a view to increasing agricultural output and significantly improving capacity utilization of processors.

Hybrid Techniques of Analyzing MRI Images for Early Diagnosis of Brain Tumours Based on Hybrid Features

Brain tumours are considered one of the deadliest tumours in humans and have a low survival rate due to their heterogeneous nature. Several types of benign and malignant brain tumours need to be diagnosed early to administer appropriate treatment. Magnetic resonance (MR) images provide details of the brain’s internal structure, which allow radiologists and doctors to diagnose brain tumours. However, MR images contain complex details that require highly qualified experts and a long time to analyse. Artificial intelligence techniques solve these challenges. This paper presents four proposed systems, each with more than one technology. These techniques vary between machine, deep and hybrid learning. The first system comprises artificial neural network (ANN) and feedforward neural network (FFNN) algorithms based on the hybrid features between local binary pattern (LBP), grey-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. The second system comprises pre-trained GoogLeNet and ResNet-50 models for dataset classification. The two models achieved superior results in distinguishing between the types of brain tumours. The third system is a hybrid technique between convolutional neural network and support vector machine. This system also achieved superior results in distinguishing brain tumours. The fourth proposed system is a hybrid of the features of GoogLeNet and ResNet-50 with the LBP, GLCM and DWT algorithms (handcrafted features) to obtain representative features and classify them using the ANN and FFNN. This method achieved superior results in distinguishing between brain tumours and performed better than the other methods. With the hybrid features of GoogLeNet and hand-crafted features, FFNN achieved an accuracy of 99.9%, a precision of 99.84%, a sensitivity of 99.95%, a specificity of 99.85% and an AUC of 99.9%.