Local Binary Patterns

The idea of designing and implementation of the real-time ATM security project came with the incidents of accessing the ATM by the unauthorized users instead of the authorized user. This project will give access to the user only after identifying the image of the user taken by the CCTV in the ATM and compare the identified image with the image of the user that was stored in the database created during the account creation which comes under the banking session of banks. In some cases the authorized user is not able to use the ATM for some emergency purposes, in such cases, the OTP is sent to the users registered mobile number and the person who came instead of the authorized user have to enter the OTP that the authorized user received. This method will reduce the risk in ATM usage by the common people. The face detection and face recognition are done using deep learning techniques and machine learning. The IOT components like Camera, RFID reader, Tag, Relay, Motor were used. The Raspberry pi 3(2015version) is used as the main component. Here the opencv is used as the platform and the python language is used for the deep learning techniques and face detection Haar cascade is used for face detection. The face recognition module is done by Local Binary Patterns (LBP) algorithm. And an alert message is sent to the authorized user as a text message if the user is found to be the third party user.

Facial nerve paralysis is a common disease due to nerve damage. Most approaches for evaluating the degree of facial paralysis rely on a set of different facial movements as commanded by doctors. Therefore, automatic recognition of the patterns of facial movement is fundamental to the evaluation of the degree of facial paralysis. In this paper, a novel method named Active Shape Models plus Local Binary Patterns (ASMLBP) is presented for recognizing facial movement patterns. Firstly, the Active Shape Models (ASMs) are used in the method to locate facial key points. According to these points, the face is divided into eight local regions. Then the descriptors of these regions are extracted by using Local Binary Patterns (LBP) to recognize the patterns of facial movement. The proposed ASMLBP method is tested on both the collected facial paralysis database with 57 patients and another publicly available database named the Japanese Female Facial Expression (JAFFE). Experimental results dem...

The method is based on recognizing that certain local binary patterns, termed are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. The Local Binary Pattern (LBP) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a circular window. A new feature set derived from the LBP, called the LBP-Constant-Symmetry (LBP-CS) and LBP-High-Symmetry (LBP-HS) are proposed for recognition of stone textures. The features are computed from each band of an isotropic color LBP Matrix for recognition. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently recognizing complex industrial products based on color and texture features.

Alzheimer disease is a form of dementia that results in memory-related problems in human beings. An accurate detection and classification of Alzheimer disease and its stages plays a crucial role in human health monitoring system. In this research paper, Alzheimer disease classification was assessed by Alzheimer's disease Neuro-Imaging Initiative (ADNI) dataset. After performing histogram equalization and skull removal of the collected brain images, segmentation was carried-out using Fuzzy C-Means (FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF), and grey matter from the pre-processed brain images. Then, hybrid feature extraction (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM)) was performed for extracting the feature values from the segmented brain images. After hybrid feature extraction, reliefF feature selection was used for selecting the optimal feature subsets or to reject the irrelevant feature vectors. Then, the selected optimal feature vectors were given as the input to a supervised classifier Support Vector Machine (SVM) to classify three Alzheimer classes of subjects; those are normal, Alzheimer disease and Mild Cognitive Impairment (MCI). The experimental outcome showed that the proposed methodology performed effectively by means of sensitivity, accuracy, specificity, and f-score. The proposed methodology enhanced the classification accuracy up to 2-20% compared to the existing methodologies.

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin ...

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different pos...

Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are weighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS,  SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations.

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.

Alzheimer disease is a form of dementia that results in memory-related problems
in human beings. An accurate detection and classification of Alzheimer disease and its
stages plays a crucial role in human health monitoring system. In this research paper,
Alzheimer disease classification was assessed by Alzheimer’s disease Neuro-Imaging
Initiative (ADNI) dataset. After performing histogram equalization and skull removal
of the collected brain images, segmentation was carried-out using Fuzzy C-Means
(FCM) for segmenting the white matter, Cerebro-Spinal Fluid (CSF), and grey matter
from the pre-processed brain images. Then, hybrid feature extraction (Histogram of
Oriented Gradients (HOG), Local Binary Patterns (LBP), and Gray-Level Co-
Occurrence Matrix (GLCM)) was performed for extracting the feature values from the
segmented brain images. After hybrid feature extraction, reliefF feature selection was
used for selecting the optimal feature subsets or to reject the irrelevant feature
vectors. Then, the selected optimal feature vectors were given as the input to a
supervised classifier Support Vector Machine (SVM) to classify three Alzheimer
classes of subjects; those are normal, Alzheimer disease and Mild Cognitive
Impairment (MCI). The experimental outcome showed that the proposed methodology
performed effectively by means of sensitivity, accuracy, specificity, and f-score. The
proposed methodology enhanced the classification accuracy up to 2-20% compared to
the existing methodologies.

ABSTRACT We propose a novel approach using Complete Local Binary Pattern feature generation method for facial expression recognition with the help of Multi-Class Support Vector Machine. Complete Local Binary Pattern method is an extended version of Local Binary Pattern method with a little difference. LBP feature considers only signs of local differences, whereas CLBP feature considers both signs and magnitude of local differences as well as original center gray level value. CLBP and LBP have same computational complexity while CLBP performs better facial expression recognition over LBP using SVM training and multiclass classification with binary SVM classifiers. The experimental result demonstrate the average efficiency of recognition of propose method (35 images) with CLBP is 86.4%, while with LBP and CCV is 84.1255% and 75.83% in the JAFFE database.

In this paper, a new algorithm which is based on the graph cut theory and local binary patterns (LBP) for content based image retrieval (CBIR) is proposed. In graph cut theory, each node is compared with the all other nodes for edge map generation. The same concept is utilized at LBP calculation which is generating nine LBP patterns from a given 3×3 pattern. Finally, nine LBP histograms are calculated which are used as a feature vector for image retrieval. Two experiments have been carried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Brodatz database (DB1), and MIT VisTex database (DB2). The results after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.

Defect detection is one of the problems in image processing and many different methods based on texture analysis have been proposed. The two dimensional local binary pattern approach provides discriminate features for texture analysis. In this paper for the first time, a method is proposed for detecting abnormalities in surface textures based on single dimensional local binary patterns. The proposed approach includes two steps. Firstly, in training step, single dimensional local binary patterns is applied on full defect-less surface images and the basic feature vector is calculated. Then, by image windowing and computing the non-similarity amount between these windows and basic vector, a threshold is computed for defect-less surfaces. Finally, in testing step, by using the defect-less threshold the defects are detected on test images. High detection rate, and low computational complexity are advantages of the proposed approach. The proposed approach is fully automatic and all of the...

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a ...

This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis (PCA) and Local binary pattern (LBP) algorithms. Experiments were carried out of using Japanese female facial expression (JAFFE) database. In all experiments conducted using JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with an average recognition rate of 90% under the same experimental setting.

In this paper we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using ge-netic programming (GP) and rotationally invariant local binary patterns (LBP). Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The seg-mentation algorithm achieved a mean ( ± standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the seg-mented boundary from the true boundary was 1.12 ± 0.463mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00mm. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

The extraction of required facial expression features from the human face image is an important task for face recognition. Facial expressions are natural means of communications between humans and play a major role in pattern recognition and image processing. Research in facial expression recognition has considered seven basic facial expressions namely anger, disgust, fear, happy, sad, surprise and neutral. For the face expression recognition three main phases are used: face detection, facial feature extraction and facial feature classification and recognition. In this paper we studied the different facial feature recognition techniques proposed by various researchers: Gabor, DWT, DCT, RSST Segmentation and Local Binary Patterns(LBP) features with their advantages and disadvantages.

Plaques in the carotid artery result in stenosis, which is one of the main causes for stroke. Patients have to be carefully selected for stenosis treatments as they carry some risk. Since patients with symptomatic plaques have greater risk for strokes, an objective classification technique that classifies the plaques into symptomatic and asymptomatic classes is needed. We present a computer aided diagnostic (CAD) based ultrasound characterization methodology (a class of Atheromatic systems) that classifies the patient into symptomatic ...

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.