Linear Binary Pattern Research Articles

Recognition of Handwritten Digits using Convolutional Neural Network and Linear Binary Pattern

Over the past few years there has been a tremendous developments observed in the field of computer technology and artificial intelligence, especially the use of machine learning concepts in Research and Industries. The human effort can be further more reduced in recognition, learning, predicting and many other areas using machine learning and deep learning. Any information which has been handwritten documents consisting of digits in digital form like images, recognizing such digits is a challenging task. The proposed system can recognize any handwritten digits in the document which has been converted into digital format. The proposed model includes Convolutional Neural Network (CNN), a deep learning approach with Linear Binary Pattern (LBP) used for feature extraction. In order to classify more effectively we also have used Support Vector Machine to recognize mere similar digits like 1 and 7, 5 and 6 and many others. The proposed system CNN and LBP is implemented on python language; also the system is tested with different images of handwritten digits taken from MNIST dataset. By using proposed model we could able to achieve 98.74% accuracy in predicting the digits in image format.

Automated Diabetic Retinopathy Detection using CS Based SVM Techniques

Diabetes is one of the metabolic maladies where a patient has high glucose either brought about by body inability to create enough insulin or the cells inability to react to deliver insulin. This ceaseless ailment may prompt long haul inconveniences and death. It can cause high danger of kidney disappointment, nervous system harm, visual impairment and coronary illness. During the ongoing years there have been numerous examinations on programmed finding of diabetic retinopathy utilizing a few features and techniques. In this work at first the color fundus image can be utilized for processing, methods, for example, Median filtering, Morphological transformation, or Histogram equalization used to improve the nature of the image. Then to detect microaneurysms, blood vessels and optic disc using the techniques like morphological thresholding transformation, and the features are extracted from Grey level Co-occurrence Matrix (GLCM), Gabor Feature extraction and Linear Binary Pattern (LBD).At long last, for classify the various phases of diabetic retinopathy, SVM (support Vector Machine) Algorithm will be utilized, the outcomes are optimized by Cuckoo Search (CS) calculation.

Performance Analysis of Face Recognition Techniques for Feature Extraction

Due to increasing applications which need security, face is the most natural way to recognize the person. This paper presents the performance analysis of different face recognition techniques. Linear Binary Pattern (LBP), Gabor Wavelet, Histogram of Oriented Gradient (HOG) techniques are used for feature extraction on two standard databases of images i.e., ORL and Yale Face database. Then Principal Component Analysis (PCA) is applied for feature selection with six different distance metric functions, Cosine, Euclidean, Correlation, Cityblock, Spearman and Minkowski for similarity matching of images.

Intelligent terminal face spoofing detection algorithm based on deep belief network

In recent years, face recognition has rapidly developed in the field of smartphones and control systems. It has been used to unlock the telephone and face-payment applications. With such rapid development, more and more demands are placed on the security of face recognition. However, face biometric-based recognition technologies are still vulnerable to spoofing attacks. Thus, developing robust and reliable antispoofing attack detection is critical to guarantee the security of facial analysis-based authentication. As deep learning techniques have achieved satisfactory performances in computer vision, they can also be employed to face spoofing detection. We present a multichannel linear local binary pattern optimization algorithm, which combines with Lucas–Kanade optical flow algorithm. The extracted facial features are fused and sent to a deep belief network classifier for classification and learning, and finally tested on the MSU-mobile facial spoofing database. Compared with existing deep leaning-based detection methods, our face spoofing detection algorithm has better scalability and robustness. To evaluate the performance of the proposed algorithm, the experiments are conducted on three crossed standard spoofing databases and excellent performance is also achieved.

A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques.

Biometric Recognition and Authentication is used in many applications for the secured identification of the persons. Several Researches has been carried out to strengthen the security algorithms through which the identification can be done in secured manner. With this objective, a new algorithm called Hybrid Adaptive Fusion(HAF) has been proposed which works on the principle of hybrid fusion of two feature inputs such as Hand geometry and iris of the users. As mentioned, the proposed algorithm uses the novel and hybrid fusion of feature extraction along with the accurate machine learning classifier. Effective Linear Binary Patterns (ELBP) and Scale Invariant Fourier Transform (SIFT) arestored in the databases for the further verification. The features stored are fed into the Extreme Learning machines for the detection of the verified users. This algorithm has been tested with the CASIA Image Datasets and with the different classifiers such as Neural Networks, Baiyes Networks. The proposed algorithm with ELM has better accuracy of 98.5% when compared with the other machine learning algorithms.

A Novel Feature Level Fusion Method for Classification of Remote Sensing Images

Feature level fusion approach is utilized in this paper to classify remote sensing images. Texture features are extracted from panchromatic images using mixed Gabor filter (GB), fast gray level co-occurrence matrix (GLCM) and linear binary pattern (LBP). The resultant texture features are classified using nearest neighbor (k-NN) classification method. Spectral features are extracted from the MS image and segmented using over segmented k-means algorithm with novel initialization (OSKNI). Finally the segmented MS image and grid classified PAN image are fused to get the final classified result. To evaluate the performance of the proposed method we used kappa statistics like, Users Accuracy (UA), Producer’s accuracy (PA), Overall classification accuracy (OCA), Expected Classification Accuracy (ECA) and KHAT values. Keywords: Texture, spectral, panchromatic, multispectral, segmentation Cite this Article Shashidhar Sonnad, Lalitha YS. A Novel Feature Level Fusion method for Classification of Remote Sensing Images. Journal of Remote Sensing & GIS. 2019; 10(1): 58–65p.

Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Facial recognition is now a days is very emerging topic. The most challenging task for normal human being is to follow the facial recognition retrieval model for correct match in the least running time. Especially while dealing with moving or non-static environment like live video, webcam recording, or accessing real-time video in which facial features are not clear as to take as input image. Comparison between two different approached has been presented in this paper, linear binary pattern Haar technique is compared by deep learning using neural networks, different images of different persons has been taken, deep learning approach is more accurate according to different angles the video taken or any distance the video captured either for moving or static objects either from Mobile Video Camera or CCTV Camera then LPBH approach.

The most discriminant subbands for face recognition: A novel information-theoretic framework

In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the selection of features or/and classifiers.

Vision based Office Automation and Security System using Machine Learning and Internet of Things

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.

Classification Performance of Linear Binary Pattern and Histogram Oriented Features for Arabic Characters Images: A Review

Classification Performance of Linear Binary Pattern and Histogram Oriented Features for Arabic Characters Images: A Review

A multimodal authentication for biometric recognition system using hybrid fusion techniques

Biometric Recognition and authentication is used in every application for the secured identification of the persons. Several Researches has been carried out in the past decade that concerns only to enhance the accuracy of biometric algorithms for various kinds of features by ignoring the aspects of robustness and reliability. With this objective, new algorithm called effective linear scale authentication has been proposed which works on the principle of hybrid fusion of two feature inputs such as Hand geometry and iris of the users. The two different techniques have been adopted for the Feature Extraction. One is effective linear binary patterns and other is scale invariant fourier transform and it is stored in the databases for the further verification. This algorithm has been tested with the CASIA Image Datasets and the results had clearly proven that the novel idea of fusing the local features to address the scalar and angular inefficiencies of the existing methods.

Intelligent Facial Emotion Recognition System Using Linear Binary Pattern (LBP)

Intelligent Facial Emotion Recognition System Using Linear Binary Pattern (LBP)

Face Recognition LBP Feature Extraction and PCA Techniques

Face recognition is one of the security methods in biometric system in world wide area. Different mathematical calculations are used to perform face recognition. In this paper, Linear Binary pattern is applied to the different ORL face database. Linear discriminant analysis is applied followed by linear binary pattern. In this, different component analysis and training set data reduction is done by PCA techniques. The Feature extraction & pattern recognition is done by LBP method. The classification rate is achieved up to 94.7

Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland

Automated robotic weeding of grassland will improve the productivity of dairy and sheep farms while helping to conserve their environments. Previous studies have reported results of machine vision methods to separate grass from grassland weeds but each use their own datasets and report only performance of their own algorithm, making it impossible to compare them. A definitive, large-scale independent study is presented of all major known grassland weed detection methods evaluated on a new standardised data set under a wider range of environment conditions. This allows for a fair, unbiased, independent and statistically significant comparison of these and future methods for the first time. We test features including linear binary patterns, BRISK, Fourier and Watershed; and classifiers including support vector machines, linear discriminants, nearest neighbour, and meta-classifier combinations. The most accurate method is found to use linear binary patterns together with a support vector machine.1This research was supported in part by the Innovate UK project IBEX2: Autonomous robot weed spraying for less favoured areas, Grant No. 131790.1

Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images

Malaria, being a life-threatening disease caused by parasites, demands its rapid and accurate diagnosis. In this paper, we develop a computer-assisted malaria-infected life-cycle stages classification based on a hybrid classifier using thin blood smear images. The major issues are: feature extraction, feature selection and classification of erythrocytes infected with different life-cycle stages of malaria. Feature set (134 dimensional features) has been defined by the combination of the proposed features along with the existing features. Features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R–G color channel difference histogram and Gabor features are the newly proposed features in our system. In the feature selection, a two-stage algorithm utilizing the filter method to rank the feature, along with the incremental feature selection technique, has been analyzed. Moreover, the performance of all the individual classifiers (Naive Bayes, support vector machine, k-nearest neighbors and artificial neural network) is evaluated. Finally, the three individual classifiers are combined to develop a hybrid classifier using different classifier combining techniques. From the experimental results, it may be concluded that hybrid classifier formed by the combination of SVM, k-NN and ANN with majority voting technique provides satisfactory results compared to other individual classifiers as well as other hybrid model. An accuracy of 96.54 ± 0.73% has been achieved on the collected clinical database. The results show an improvement in accuracy (11.62, 6.7, 3.39 and 2.39%) as compared to the state-of-the-art individual classifiers, i.e., Naive Bayes, SVM, k-NN and ANN, respectively.

Face Recognition using SIFT Key with Optimal Features Selection Model

Facial expression is complex in nature due to legion of variations present. These variations are identified and recorded using feature extraction mechanisms. The researchers have worked towards it and created classifiers for identifying face expression. The classifiers involve Principal component analysis (PCA), Local Polynomial approximation (LPA), Linear binary pattern (LBP), Discrete wavelet transformation (DWT) etc. The proposed work deals with the new classifier using SIFT key with genetic algorithm to identify distinct facial expression. Optimal features of existing algorithms are used within the proposed work. Also comparison of existing techniques such as LBP, PCA and DWT is presented with SIFT key with genetic algorithm. The results show that proposed classifier gives better result in terms of recognition raet.

Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear

This paper aims to develop the computer assisted malaria infected erythrocyte classification based on a hybrid classifier. The major issues are feature extraction, optimal feature selection and erythrocytes classification. 54 dimensional features formed by the combination of the proposed features and the existing features have been used to define the feature set. The features such as prediction error, co-occurrence of linear binary pattern, chrominance channel histogram, R-G color channel difference histogram are the newly proposed features in our system. For feature selection, the different techniques have been explored to obtain the optimal feature set. Further, the performance of the different individual classifiers (SVM, k-NN and Naive Bayes) and hybrid classifier, obtained by combining the individual classifiers, is evaluated using the optimal feature set. Using the proposed optimal feature set and hybrid model, better performances (i.e. sensitivity 95.86%, accuracy 98.5%, F-score 93.82%) have been achieved on the collected clinical database. Based on the experimental results it may be concluded that hybrid classifier provides satisfactory results with an improvement in sensitivity (1.09%, 12.04%, 0%), accuracy (0.12%, 1.15%, 1.27%) and F-score (0.7%, 5.77%, 4.61%) as compared to the individual classifiers i.e. SVM, k-NN and Naive Bayes respectively.

An Efficient Reconfigurable Architecture for Fingerprint Recognition

The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters.

Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the two-dimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face non-uniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

HISTOGRAM OF TRANSITION FOR HUMAN HEAD RECOGNITION

The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. This feature is relied on foreground extraction. In this paper we evaluate some pre-processing to get foreground extraction before we calculate the histogram of transition.We evaluate the performance of recognition rate in related with preprocessing of input image, such as color, size and orientation. We evaluate for Red-Green-Blue (RGB) and Hue-saturation-Value (HSV) color image; multi scale of 10×15 pixels, 20×30 pixels and 40×60 pixels; and multi orientation angle of 315o, 330o, 345o, 15o, 30o, and 45o.For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradients (HOG) and Linear Binary Pattern (LBP). The experimental results show Histogram of Transition robust for changing of color, size and orientation angle.