LBP and SIFT based facial expression recognition

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.

This paper highlights the ability of residual convolutional neural network (ResNet) at classifying railway shelling defect dataset without the requirement for handcrafted features. A 41-convolutional layers ResNet with the residual learning block is introduced. Bottleneck architecture of three convolutional layers is used in ResNet to decrease the computation cost. VGG convolutional neural network (VGGNet) classifier and Support Vector Machine (SVM) classifiers based on Histogram of Oriented Gradient (HOG), Local Binary Patterns (LBP) and Scale-invariant Feature Transform (SIFT) are compared with ResNet classifier at classifying our dataset. The performance of ResNet presented in this paper achieves 95% TOP-1 accuracy in testing dataset. In comparison, the result of ResNet is better than VGGNet with 92% TOP-1 accuracy and HOG (42.88%), LBP (52.26%), SIFT (60.69%). In the context of designing neural computing models for ResNet analysis, this paper shows that ResNet used in our experiment is able to not only achieve the high accuracy in railway shelling defect testing dataset, but also has a faster testing speed than the other classifiers.

This paper presents a novel method for stereo matching, We introduce the extension version of the well-known local binary pattern (LBP) feature called local ternary pattern (LTP) serving as s distinctive local descriptor of local image feature point region detected by the scale invariant feature transformation (SIFT) detector. In our experiments, our descriptor combined with the advantage of SIFT and rotation invariant uniform LTP performs favourably compared to SIFT and LBP, and computes more efficient than SIFT.

SIFT (scale invariant feature transform) is one of the most robust and the widely used image matching algorithms based on local features. It has been used in the aspect of the cameras target matching. For its high complexity, this paper proposes a target matching method for multiple cameras by combining SIFT and the rotation-invariant LBP (local binary patterns). The object detection is obtained by improved Back-ground subtraction technique, and particle filter is used for region-based object tracking. The descriptor combining SIFT and the rotation-invariant LBP is calculated for every object region. The object matching is obtained by comparing the distance of feature matrix. Experimental results show that the proposed algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm, and it is robust in various scenes.

Extraction of discriminate features which could represent the facial expression accurately plays a vital role in effective Facial Expression Recognition (FER). Although much progress has been made, selecting the discriminate features is still a challenging and interesting problem in the FER system. In this paper, we propose a new FER method, which uses the active shape mode (ASM) algorithm to landmark the key points of face, then extracts local binary patterns (LBP) features around the key points and uses the Multi-Task Learning (MTL) algorithm to select the discriminate patches to represent facial expression accurately. Then we use uses support vector machine (SVM) classifier to predict the facial emotion. Experiments on the Extended Cohn-Kanada database show that the proposed method has a promising performance and realizes the recognition rate of 96.44%.

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in‐plane head rotation and break up facial region from background. Then, spatial‐temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one‐versus‐one strategy based multiclass support vector machine (SVM) classifier is applied to classify facial expressions. Experiments on Cohn‐Kanade (CK) + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state‐of‐the‐art methods on CK+, MMI, and Oulu‐CASIA VIS datasets, our proposed framework performs better.

Depression is a common mental disease characterized by significant sadness and feeling blue all the time. At present, most classifications and predictions of depression rely on different characteristics. Comparing with the previous work, we use local binary pattern (LBP) and signal singular spectrum analysis (SSA) technology to extract features from the original signal. Firstly, the LBP signal is obtained by encoding the segmented signal. Then, we use SSA to decompose and reconstruct the LBP signal to remove noise and divide the frequency band. Finally, we feed the data of each frequency band to K-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and extreme learning machine (ELM) for classification. The experimental results show that LBP and SSA features achieve the best classification effect on SVM, and the accuracy of beta band is the highest with 99.24% accuracy, 99.34% sensitivity and 99.12% specificity respectively.

Humans interact socially with the help of facial expressions. Even health states or pains are reflected through facial expressions and hence can be useful in healthcare. Here, a facial expression recognition system is proposed. The bandlet transform is performed on face image to generate quadtree. Then on the output of bandlet transform centre symmetric - local binary pattern (CS-LBP) is applied. A feature vector of the image is generated by taking the histogram of CS-LBP. The support vector machine (SVM) is used to classify expressions in six categories. The experiments are performed using a publically available CK+ dataset. The initial results with LBP and CS-LBP are reported.

Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of LBP algorithm with low-order polynomial SVM are better than Gauss-kernel function.

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

In the field of quantitative microscopy, textural information plays a significant role very often in tissue characterization and diagnosis, in addition to morphology and intensity. The objective of this work is to improve the classification accuracy based on textural features for the development of a computer assisted screening of oral sub-mucous fibrosis (OSF). In fact, the approach introduced is used to grade the histopathological tissue sections into normal, OSF without dysplasia (OSFWD) and OSF with dysplasia (OSFD), which would help the oral onco-pathologists to screen the subjects rapidly. The main objective of this work is to evaluate the use of Higher Order Spectra (HOS) features and Local Binary Pattern (LBP) features extracted from the epithelial layer in classifying normal, OSFWD and OSFD. For this purpose, we extracted twenty three HOS features and nine LBP features and fed them to a Support Vector Machine (SVM) for automated diagnosis. One hundred and fifty eight images (90 normal, 42 OSFWD and 26 OSFD images) were used for analysis. LBP features provide a good sensitivity of 82.85% and specificity of 87.84%, and the HOS features provide higher values of sensitivity (94.07%) and specificity (93.33%) using SVM classifier. The proposed system, can be used as an adjunct tool by the onco-pathologists to cross-check their diagnosis.

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.