Entropy based Local Binary Pattern (ELBP) feature extraction technique of multimodal biometrics as defence mechanism for cloud storage

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

In the member of mud crab, species of Scylla are the most traded seafood commodity in Asia and the culturing practice is already done in most of Asia country a few years ago. The demand for mud crabs has increased rapidly over the last decade, providing great potential for the development of the mud crab aquaculture industry. But, there is still unsolved problem where limitation of knowledge in identification is limited due to similar colours and feature characteristic. Thus, this study proposed an automatic technique that can evaluate and produce the subset of mud crab genus Scylla features by using Local Binary Pattern (LBP) as a feature extraction tool. The main objective of the study is to find the optimal subset of mud crab genus Scylla features from a carapace images dataset. Based on 153 extracted features chosen by LBP features selection methods, the accuracy rates of three classification algorithms were obtained for analysis. The results from the MatLab experiment demonstrated that, the LBP method produced an accuracy under 60% for entire classifier.

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

This paper presents a novel face recognition method by using the new image representations. While the commonly used gray-scale image is derived from the linear combination of R, G, and B color component images, the new image representations are derived from the Principal Component Analysis (PCA) tranform upon the hybrid configurations of different color component images. Compared to the correlated color space RGB, the correlations in other configurations of color components (such as RCrQ, YIQ, YCbQ, and so on) are reduced and hence the diversities among their misclassification outputs are enhanced. The new image representations, which inherit advantages from all the individual color components, are thus more invariable than the gray-scale image to the image variations for the face recognition task. Furthermore, we propose to encode the facial information from the new image representations by using an effective Local Binary Pattern (LBP) feature extraction method, which extracts and fuses the multi-resolution LBP features. Finally, the resulting LBP features undergo the Fisher discriminant analysis for face recognition. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows the proposed method, which achieves the face verification rate of 83.41% at the false accept rate of 0.1%, performs better than some recent face recognition methods.

The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition, LBP cannot distinguish between a strong and a weak pattern. In order to enhance the LBP approach, Fuzzy Local Binary Patterns (FLBP) is proposed. In FLBP, any neighborhood does not represented only by one code, but, it is represented by all existing codes with different degrees. In FLBP, any fuzzy Intersection and Union operators may be used. In this study, the following operators are applied and their results are compared together: Dot-Sum, Min-Max and normalized Min-Max. Based on the extensive experiments, the fuzzy Min-Max operators are more useful and can improve the accuracy in the application of Facial Expression Recognition (FER) about 4% (i.e., form 82.98% to 86.88%).

Plant diseases are one of the main causes to reduce the quality and quantity of crop yield. Traditional expert eye necked opinion method is used to recognize the crop diseases. In rural areas getting experts, opinion is a more tedious job. The automated system is required to recognize the disease accurately and quickly. In automated systems, feature extraction played an important role. Many feature extraction techniques are suggested for the classification of plant diseases. According to the literature, the local binary pattern (LBP) method has good object recognition and classification performance. In our work, we have compared a study of extracted features based on the LBP and gray-level co-occurrence matrix (GLCM) feature with color features for the recognition of banana plant leaf diseases. The experiments were performed using different banana leaf diseases like banana yellow sigatoka, bunchy top, cucumber mosaic virus, and healthy leaves. The result shows that the features extracted using LBP are more accurate as compared to GLCM when compared with the SVM classifier. KNN gives more accurate results with GLCM.

A face tracking algorithm based on LBP (Local Binary Pattern) histograms and particle filtering is presented in this paper. The mean shift algorithm is adopted to locate the face region coarsely. Then the particles can be propagated efficiently with small random noise added. Since color histograms as face representation can be disturbed easily by the background with skin color. LBP histograms are used as the representation of faces. In order to alleviate the influence of light variance, the face template is updated under certain condition. The experimental results show that the proposed algorithm is superior to the CAMSHIFT algorithm and the particle filter based on color histograms.

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

Automatic voice based disease detection method using one dimensional local binary pattern feature extraction network

Computer-aided diagnosis (CAD) system for breast ultrasound can effectively assist doctors in classifying benign and malignant of breast tumors. CAD system increases the work efficiency and diagnostic accuracy, and it also reduces the rate of misdiagnosis. This paper proposes a method based on a histogram of oriented gradients (HOG), local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) feature extraction combined with machine learning classifier – support vector machine (SVM) to classify benign and malignant breast tumor ultrasound images. Besides, due to the lack of the latest and comprehensive ultrasonic image dataset, this work collected a latest ultrasound image dataset, which considers 350 benign lesions and 257 malignant tumors proven by biopsy. The ability of the classifier to predict the pathological classes is measured by the receiver operating characteristic (ROC) curve and obtain good classification result.

In this work we focus on developing an accurate and lightweight automated diagnostic system for classifying abnormalities in breast cancer mammogram images using the Local Binary Patterns (LBP) feature extraction method at the heart of a machine learning model. We empirically show the success of the approach in recognising the presence or absence of abnormality(ies) with high predictive performance in terms of accuracy, precision, recall, and F1-score, and against other features extraction methods employed within different a number of classifiers. The reported results show a minimum of 13% performance gap in favour of our selected approach to the closest model. Our approach exceeds 90% in most of the models when applied over DDSM, the breast cancer mammography benchmark dataset.

Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein–protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.