Local Histogram Research Articles

Detection & recognition of veiled and unveiled human face on the basis of eyes using transfer learning.

Face detection and recognition are the most substantial research areas in computer vision and transfer learning due to the inspiring nature of faces as an object. In this paper, we show that we can obtain promising results on the standard face databanks when the features are extracted merely from the eye. The contributions of this work are divided into three parts, specifically face detection, eyes detection and recognition for individual identification. The key features for face recognition, used in this study are the eyes, nostrils, and mouth. The key features for eyes recognition are center of left eye, center of right eye, midpoint of eyes and extraction of eyebrows. Extracted Local Binary Pattern Histogram (LBPH) method is used to extract the facial features of face images whose computational complexity is very low and these features contain simple pixel values. Furthermore, neighborhood pixels are calculated to extract effective facial feature to realize eyes recognition and person verification. This study is able to identify an individual on the basis of even a single eye. The algorithm finds the brighter eye from the face and then, on the basis of that eye, the person is identified and the name of person is provided. The experimental results of this study show that faces are recognized accurately and LBPH method has achieved 98.2% accuracy.

A local activation time histogram-An invaluable tool to diagnose a rare and complex atrial flutter mechanism.

A local activation time histogram-An invaluable tool to diagnose a rare and complex atrial flutter mechanism.

Covid-19 detection from radiographs by feature-reinforced ensemble learning.

SummaryThe coronavirus (Covid‐19) epidemic continues to have a negative influence on the global population's well‐being and health. Scientists in many fields around the world are working non‐stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X‐ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS‐CoV‐2 infection from X‐ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X‐ray images categorized as Normal, Pneumonia, and Covid‐19. The images in the dataset were sized in four different ways, 32 × 32, 64 × 64, 128 × 128, and 256 × 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10‐fold cross‐validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four‐times smaller (e.g., 32 × 32 vs. 128 × 128) than those used in the ResCNN. This shows that the model can give ideal results with lower computational cost.

IDENTIFIKASI PENGENALAN WAJAH UNTUK SISTEM PRESENSI MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR)

Attendance is an activity that is so important and cannot be separated from a teaching and learning activity to calculate and see student attendance. In this Final Project, research on the automatic attendance system is carried out through facial recognition identification (face recognition) using a webcam as a system input, then the resulting image capture results from each image will be processed through feature extraction using the LBPH (Local Binary Pattern Histogram) method and classification with the KNN (K-Nearest Neighbor) method and the help of OpenCV library-based Python software. The research in this Final Project obtained an average accuracy value in facial recognition using LBPH (Local Binary Pattern Histogram) of 93.9%, with an average FAR value of 4.66% and an average FRR value of 1.33%. For the classification of KNN (K-Nearest Neighbor) using Euclidean Distance when k = 1 obtained an accuracy of 100% with a computation time of 34 ms, at the time of k = 3 an accuracy of 98% with a computation time of 37 ms was obtained and at the time of k = 5 an accuracy of 88% with a computation time of 42 ms.

Facial Recognition Attendance System Using Deep Learning

Abstract: Face authentication is one of the critical unsolved problems in computer science, a lot of time and energy is spent to invent a robust solution for it. Face authentication can play a significant role in security, Biometric verification, auto detection of criminals in a crowd, etc. The paper presents a unique model that authenticates the faces in the image, and securely shares the images. Firstly, the deep learning algorithm identifies all the faces in the image, extracts the invariant features of each face. The invariant feature of the face is studied to compute the emotions. The model captures the friends list of the user and securely shares the image to the known persons in the image.The main purpose of this project is to enhance the relationship between the student and the teaching staff. Due to pandemic every student are forced to attend the online classes, even though some students are tend to bunk the classes by attending those session by use some other else. In order to stop this we developed an windows application to take facial attendance and attending the class. The current old system has a lot of ambiguity that caused inaccurate and inefficient of attendance taking. Many problems arise when the authority is unable to enforce the regulation that exists in the old system. Thus, by means of technology, this project will resolve the flaws existed in the current system while bringing attendance taking to a whole new level by automating most of the tasks. The technology working behind will be the face recognition system. In the old system they use LDA. Although LDA is one of the most common data reduction techniques, it suffers from two main problems: The Small Sample Size (SSS) and linearity problems. We improvised the facial recognition using LBPH – (Local Binary Pattern Histograms) - Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. This algorithm is so fast and reliable and the result prediction we have utilized a regression algorithm.

Present-Ma’am: An Attendance Management System

Abstract: Attendance management is critical for every institution; it can determine whether or not an institution, such as educational institutions, public or commercial sectors, will be successful in the future. To maximise their performance, institutions will need to keep track of people within the organisation, such as employees and pupils. Real-Time Face Recognition is a useful way for dealing with the daily attendance of a vast number of pupils. To increase face detection and recognition performance, several algorithms and strategies have been developed, however, our suggested model uses the LBPH (Local binary pattern histogram) methodology, which is implemented in Python using the OpenCV organization and employs the tinker GUI interface for user interface needs.

Face Recognition and Automatic Attendance Management System using Machine Learning Techniques

An automated attendance management system maintains a record of the attendance of a particular student, whether he/she is present in the class. The most important element of the virtual platform for students is maintaining attendance in an educational institution. When done manually, it wastes a substantial amount of time and effort. Due to a lack of a reliable attendance mechanism, there were also numerous fraudulent attendances. The face is the most significant feature that distinguishes a person. As a result, the focus of this study is on an attendance system that uses a real time face identification method based on the frontal face recognition idea, as well as an efficient Haar cascade algorithm based on an open-source image processing framework as well as the Local Binary Pattern Histogram.

Face recognition using LBPH algorithm

Face acknowledgment is a strategy which recognizes an individual in light of the profile or elements of the substance of that individual. LBPH (Local Binary Patterns Histogram) is a strategy to identify and perceive the substance of an individual. In LBP, first, some piece of a picture which is in grayscale is taken as 3×3 window size and the pixel worth of neighborhood is contrasted and the focal pixel worth and afterward the twofold worth is doled out which is then changed over to a decimal worth. LBP is then joined with histograms as is called a LBPH calculation. GPU (Graphics Processing Unit) is an electronic circuit which is more remarkable than CPU (Central Processing Unit). A front facing face and side profile face acknowledgment utilizing LBPH calculation are executed on GPU. The exhibition of the CPU and GPU are then analyzed.

Intelligent framework for brain tumor grading using advanced feature analysis

ABSTRACT The analysis of digital pathology images, catalyzes research and automate diagnosis for improving clinical care. Regardless of the advances in high-resolution and speedy scanning devices, the clinical analysis of Whole Slide Image (WSI) remains mainly as the work of an image analyst. The complications when working on these images are primarily, its enormous image size, as well as the heterogeneous nature of tissue regions. These reasons may lead to inefficient computer analysis. We propose a comprehensive algorithm to analyse, these large images by using small tile image analysis. The small tile images can be learnt individually in detail, rather than processing the whole high resolution WSI. The analysis of these small tile images helps us configure, the localised pathology image characteristics. The work flow involves extracting Perceptual Features (PF), Fourier Local Binary Patterns (FLBP), Histogram, Gabor and Gray Level Co-occurrence Matrix (GLCM) features from the tiled WSI. Then, the multidimensional feature set is provided to the classifiers like K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR) for performance analysis. We evaluated our predictive models by automatically classifying image datasets (186 and 124) of brain tumour cases, using 10 fold cross validation. The model classified the images into Low Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). It gives the highest accuracy of 93.5% for a feature combination of PF+FLBP+GLCM+GABOR with linear Support Vector Machine (linSVM) and Area under the Curve (AUC) of 0.9741 for the 186 image dataset. Also linSVM gives an accuracy of 96.8% with various feature combinations for the 124 image dataset. We did an exhaustive study on the performance of various sets of texture descriptors and classification models. This resulted in finding an optimised classification approach with a combined feature set that outperformed the original feature set classification. The proposed work is unique because it combines a wide range of feature extracting methods and an exhaustive list of classifiers showcasing the best method. The idea that, rather than selecting a region of interest from WSI for grade designation, all the regions in a slide are considered. This enables the involvement of each tile in making the final diagnostic decision, considering the heterogeneous nature of WSI. Our work can be extended for different tumour subtypes. Also, the experiment can be scaled with different sample sizes and finally the method works without a pathologist input.

Alat Pendeteksi Suhu Tubuh dan Wajah (Kebutuhan Bukti Kehadiran) Berbasis Data

This research leads to the manufacture of presence tools by implementing a face recognition and detection system, and is equipped with body temperature measurements. In this study, for face detection and recognition, the Local Binary Pattern Histogram (LBPH) Algorithm and Haar Cascade of Classifier were applied. The algorithm's working system performs face detection and recognition quickly with the use of a webcam. The scope of this research is to conduct database preparation, training, detection, recognition, and face identification. For measuring body temperature using the MLX90614 Infrared temperature sensor, this temperature sensor utilizes infrared radiation, this sensor can automatically measure energy in infrared radiation which will become a temperature scale. The testing process carried out in this study uses 100 databases of facial images that will be read during the detection process. From the test results, the face detection and recognition system will work optimally at a distance of 30-100 cm from the Web cam. while the body temperature measurement system will be more accurate at a distance of 5-15 cm from the temperature sensor MLX90614.

Facial expression recognition using HOG and LBP features with convolutional neural network

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.

Investigation of acoustic and visual features for pig cough classification

The precise detection of pig cough is a crucial step for establishing an early warning system for pig respiratory diseases. With regard to high precision pig cough recognition, feature extraction and selection are of importance. However, few studies have investigated both acoustic and visual features of pig vocalisations as input features. In this paper, we proposed a novel feature fusion method which fusing acoustic and visual features to achieve an enhanced pig cough recognition rate. We firstly extracted acoustic features from audio signals, including root-mean-square energy (RMS), mel-frequency cepstral coefficients (MFCCs), zero-crossing rates (ZCRs), spectral centroid, spectral roll-off, spectral flatness, spectral bandwidth and chroma. Then, constant-Q transform (CQT) spectrograms were employed to extract visual features involving local binary pattern (LBP) and histogram of gradient (HOG). Subsequently, a hybrid feature set was created by combining acoustic and visual features. In this stage, Pearson correlation coefficient (PCC), recursive feature elimination based on random forest (RF-RFE) and principal component analysis (PCA) were exploited for dimensionality reduction. Finally, support vector machine (SVM), random forest (RF) and k-nearest neighbours (KNN) classifiers were used to conduct a performance evaluation. It is shown that the fused acoustic features (Acoustic) combined with LBP and HOG (A-LH) achieved 96.45% pig cough accuracy. The results reveal that the fusion feature set outperforms acoustic and visual features alone. • Both acoustic and visual features were investigated for pig cough. • A hybrid feature vector created by early feature fusion enhanced recognition performance of pig cough. • Feature selection and extraction were employed to choose the optimal feature set. • The proposed method reached an accuracy of 96.45%.

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

<p><span>The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.</span></p>

MONITORING OF PRIVATE SPACE WITH THE LOCAL BINARY PATTERN HISTOGRAM ALGORITHM

Abstract–The main goal of this project is to create an application for security purposes. Because of this, many individuals who may or may not know us are gaining access to our personal workspace. There are other difficulties, such as losing important papers and valuables. Even the most advanced technology, such as a fingerprint sensor lock, may be easily opened. As a result, we will create an AI-based security room and locker system to address this issue. This project used artificial intelligence to discover and evaluate people, as well as track them using our ongoing initiative. We tackle the current system problem in the proposed work using LBPH (local binary pattern) and machine learning approaches to lower the recognition time of many items in less time. Keywords— Face Recognition,Face Detection, Local Binary pattern Histogram Algorithm,Media Pipe.

Human Face Identification Using Haar Cascade Classifier and LBPH Based on Lighting Intensity

The problem in implementing online learning during the Covid-19 era is the lack of internet access for video streaming, especially in small towns or villages. The solution idea is to minimize the video bandwidth quota by only showing emoticons. The first step of the process is the system must be able to lock the face area to be translated. This study aims to identify areas of the human face based on camera captures. The research was conducted using the Haar cascade classifier algorithm to recognize the facial area of the captured image. Then the Local Binary Pattern Histogram algorithm will recognize the identity of the face. The lighting scenario will be used as a distracting effect on the image. The results showed that based on 30 sets of images tested in bright conditions, the system was able to recognize facial identities up to 62%, normal conditions 51% and dark conditions 46%.

Feature Generalization for Breast Cancer Detection in Histopathological Images.

Recent period has witnessed benchmarked performance of transfer learning using deep architectures in computer-aided diagnosis (CAD) of breast cancer. In this perspective, the pre-trained neural network needs to be fine-tuned with relevant data to extract useful features from the dataset. However, in addition to the computational overhead, it suffers the curse of overfitting in case of feature extraction from smaller datasets. Handcrafted feature extraction techniques as well as feature extraction using pre-trained deep networks come into rescue in aforementioned situation and have proved to be much more efficient and lightweight compared to deep architecture-based transfer learning techniques. This research has identified the competence of classifying breast cancer images using feature engineering and representation learning over the established and contemporary notion of using transfer learning techniques. Moreover, it has revealed superior feature learning capacity with feature fusion in contrast to the conventional belief of understanding unknown feature patterns better with representation learning alone. Experiments have been conducted on two different and popular breast cancer image datasets, namely, KIMIA Path960 and BreakHis datasets. A comparison of image-level accuracy is performed on these datasets using the above-mentioned feature extraction techniques. Image level accuracy of 97.81% is achieved for KIMIA Path960 dataset using individual features extracted with handcrafted (color histogram) technique. Fusion of uniform Local Binary Pattern (uLBP) and color histogram features has resulted in 99.17% of highest accuracy for the same dataset. Experimentation with BreakHis dataset has resulted in highest classification accuracy of 88.41% with color histogram features for images with 200X magnification factor. Finally, the results are contrasted to that of state-of-the-art and superior performances are observed on many occasions with the proposed fusion-based techniques. In case of BreakHis dataset, the highest accuracies 87.60% (with least standard deviation) and 85.77% are recorded for 200X and 400X magnification factors, respectively, and the results for the aforesaid magnification factors of images have exceeded the state-of-the-art.

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Face recognition aims to detect, track, identify, or verify person from video or images using recognition system. Its application varies from security, authentication, social media, and so on. With the advancement of technologies, still it impedes in accuracy. The challenges involved in face recognition are variations in facial expression involve diverging light, facial changes interfered with facial hair, pose, and so on. This research work presents a new hybrid approach, which improves the accuracy in identification of a person. The accuracy of the proposed work clearly depicts that it can be best suited in real time environments for tracking persons. It can be used for applications like authentication, security, and so on. The proposed work is validated by tracking the student presence. It captures the image of the student and detect the face by pre-processing, extracting the features and recognizing the person. For recognition it uses Local Binary Pattern Histogram (LBPH) algorithm and Haar Cascade classifier. LBP Algorithm uses Histogram, which improves the accuracy of the face recognition. It is integrated with Haar classifier, which uses machine learning algorithm. Adaboost Learning algorithm, which selects small features from large set, which improves the efficiency of recognition.

Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms

Objectives. The problem of reducing the dynamic range of infrared images for their reproduction on display devices with a narrow dynamic range is considered. The method of local image histogram equalization based on the integral distribution function of brightness is investigated. To transform the brightness of a pixel, this method uses an approximation of the local alignment values of the nearest blocks of pixels of original image. This in-creases the local contrast of the image, but leads to high computational complexity, which is increasing while block size decreases. The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.Methods. Image processing methods are used.Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. The modification is based on the division of the set of image blocks into two subsets of high-priority and low-priority blocks depend-ing on their brightness statistical properties. When interpolating pixel values, high-priority blocks use local alignment values, and low-priority blocks use global alignment values. As a result, the total number of alignment vectors is reduced in proportion to the ratio of subsets sizes and the computational complexity of the dynamic range transformation is reduced.Conclusion. When changing the ratio of the number of high-priority blocks of infrared image pixels to the number of all blocks in the range of 0.25–0.75, the proposed algorithm is more efficient than global and adaptive histogram equalization algorithms.

Face Recognition and Gender Detection Using SIFT Feature Extraction, LBPH, and SVM

Face recognition and name and gender identification are challenging processes, especially when identifying perpetrators and suspects or when used in authentication systems. Machine learning and computer vision technologies are used in many fields, including security, and play an important role in face recognition and gender detection, offering valuable information to officials to rectify a situation in less time. This study used a few machine learning methods in the Labelled Faces in the Wild (LFW) database to examine their facial recognition and gender detection capacities. The LFW dataset was used to train and evaluate the Scale Invariant Feature Transform (SIFT) feature extraction method along with the Support Vector Machine (SVM) classifier and the Local Binary Pattern Histogram (LBPH) method. The result comparison from the current and other studies showed that the proposed LBPH method had higher accuracy in face recognition, while its accuracy in gender detection was very close to the ones of other, relevant studies.

Reconstructing Binary Signals from Local Histograms.

In this paper, we considered the representation power of local overlapping histograms for discrete binary signals. We give an algorithm that is linear in signal size and factorial in window size for producing the set of signals, which share a sequence of densely overlapping histograms, and we state the values for the sizes of the number of unique signals for a given set of histograms, as well as give bounds on the number of metameric classes, where a metameric class is a set of signals larger than one, which has the same set of densely overlapping histograms.