Local Gabor Features Research Articles

Retraction Note to: Investigation of rotated local Gabor features in face recognition using fusion techniques

Retraction Note to: Investigation of rotated local Gabor features in face recognition using fusion techniques

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.

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.

RETRACTED ARTICLE: Investigation of rotated local Gabor features in face recognition using fusion techniques

One of the major challenges related to face recognition is the effective face representation. In this paper, a new rotation algorithm is proposed to make the slanting face to upright position. Gabor features have been identified to be effective for face recognition. To investigate the prospective of Gabor phase and its combination with magnitude for face recognition, Rotated Local Gabor XOR patterns (RLGXP), which encodes the Rotated Gabor phase by using the local XOR pattern (LXP) operator is first proposed, which increases the effectiveness of face representation. Then, Cell-based Fishers linear discriminant (CFLD) to reduce the dimensionality of the proposed descriptor is introduced. Finally, by using CFLD, local patterns of Rotated Gabor magnitude and phase for face recognition are combined. This proposed approach is evaluated on MIT, GTAVE, PIE, CMU and Home databases. Moreover, to enhance the effectiveness of face representation through feature design (RLGXP), the Gabor filter parameters like mask size, scale and orientation are very important. The effectiveness of the rotating local Gabor model + CFLD method was verified for upslopes and uprights of different data sets. The analysis of face feature representation using the RLGXP + CFLD method basically depends on the phase range, the appropriate choice of the number of sub-cells per pattern map and size of neasret region. Here, the sensitivity analysis used for the above three parameters is best selected and executed. This paper also investigates the effect of different Gabor filter parameters on face recognition accuracy. Parameter Selection Investigation result proves that Gabor wavelets comprising 5 scales and 8 orientations have been chosen to extract Facial Feature Points (FFP). The final result shows that Rotated Local Gabor Features provides the best face recognition accuracy.

Locally lateral manifolds of normalised Gabor features for face recognition

Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state‐of‐the‐art.

Building Detection using Local Gabor Feature

Building Detection using Local Gabor Feature

Texture classification using multi-resolution global and local Gabor features in pyramid space

Texture classification plays an important role in computer vision, and Gabor filtering (GF) is a promising direction of texture classification for its desirable characteristics. However, traditional GF methods are too coarse to achieve satisfactory classification performance. To address this problem, this paper presents an effective texture classification method by combing multi-resolution global and local Gabor features in pyramid space. First, a pyramid space for each image is constructed via upsampling and downsampling to represent the images with different resolutions. Second, GF is applied to each image at different scales and orientations, and then the magnitude and phase components of filtered images are calculated. Third, the global and local Gabor features are extracted, where the global Gabor feature is represented by the mean and variance of the magnitude component, and the local Gabor feature is represented by the joint coding of both magnitude and phase components in a histogram form. Finally, the fusion of global and local Gabor features and the texture classification are implemented in the framework of nearest subspace classifier. Experimental results on CUReT and KTH-TIPS databases demonstrate that the proposed method significantly improves the performance of GF-based texture classification methods.

Hand Gesture Recognition Using Optimized Local Gabor Features

Hand Gesture Recognition Using Optimized Local Gabor Features

A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model

Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose a novel method based on generalized Gabor direction pattern (GGDP) and weighted discrepancy measurement model (WDMM) to overcome those defects. Firstly, a novel patch-structure direction pattern (PDP) is proposed, which can extract rich feature information and be insensitive to noise. Then, motivated by searching for a description method that can explore richer and more discriminant texture features and reducing the local Gabor feature vector’s high dimension problem, we extend PDP to form the GGDP method with multi-channel Gabor space. Furthermore, WDMM, which can effectively measure the feature distance between two images, is presented for the classification and recognition of image samples. Simulated experiments on olivetti research laboratory (ORL), Carnegie Mellon University pose, illumination, and expression (CMUPIE) and Yale B face databases under different illumination or facial expression conditions indicate that the proposed method outperforms other existing classical methods.

Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns

Gabor Wavelets (GW) have been extensively used for facial feature representation due to its inherent multi-resolution and multi-orientation characteristics. In this work we extend the work on Local Gabor Feature Vector (LGFV) and propose a new face recognition method called LGFV//LN//SNP, which employs local normalization filter in pre-processing stage. We propose a novel Spiking Neuron Patterns (SNP) as a dimensionality reduction method to reduce the dimensions of local Gabor features. SNP is acquired from projection of LGFV//LN features using Spike Response Model (SRM), a neuron model describing the spike behavior of a biological neuron. Results on AR, FERET, Yale B and FRGC 2.0 face datasets showed that SNP implementation delivered significant improvement in accuracy. Comparisons with several previously published results also suggested that LGFV//LN//SNP achieved better results in some tests. Additionally, LGFV//LN//SNP requires relatively smaller number of GW than LGFV//LN to produce optimal results.

Face Recognition by Exploiting Local Gabor Features With Multitask Adaptive Sparse Representation

For a human face, the Gabor transform can extract its multiple scale and orientation features that are very useful for the recognition. In this paper, the Gabor-feature-based face recognition is formulated as a multitask sparse representation model, in which the sparse coding of each Gabor feature is regarded as a task. To effectively exploit the complementary yet correlated information among different tasks, a flexible representation algorithm termed multitask adaptive sparse representation (MASR) is proposed. The MASR algorithm not only restricts Gabor features of one test sample to be jointly represented by training atoms from the same class but also promotes the selected atoms for these features to be varied within each class, thus allowing better representation. In addition, to use the local information, we operate the MASR on local regions of Gabor features. Then, by considering the structural characteristics of the face and the effects of the external interferences, a structural-residual weighting strategy is proposed to adaptively fuse the decision of each region. Experiments on various datasets verify the effectiveness of the proposed method in dealing with face occlusion, corruption, small number of training samples, as well as variations of lighting and expression.

The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for Iris Recognition

Gabor filters are generally regarded as the most bionic filters corresponding to the visual perception of human. Their filtered coefficients thus are widely utilized to represent the texture information of irises. However, these wavelet-based iris representations are inevitably being misaligned in iris matching stage. In this paper, we try to improve the characteristics of bionic Gabor representations of each iris via combining the local Gabor features and the key-point descriptors of Scale Invariant Feature Transformation (SIFT), which respectively simulate the process of visual object class recognition in frequency and spatial domains. A localized approach of Gabor features is used to avoid the blocking effect in the process of image division, meanwhile a SIFT key point selection strategy is provided to remove the noises and probable misaligned key points. For the combination of these iris features, we propose a support vector regression based fusion rule, which may fuse their matching scores to a scalar score to make classification decision. The experiments on three public and self-developed iris datasets validate the discriminative ability of our multiple bionic iris features, and also demonstrate that the fusion system outperforms some state-of-the-art methods.

A novel texture description by local multi-channels Gabor comprehensive patterns

The texture description is an important research topic in the pattern recognition region. A new texture description algorithm using Local Multi-channels Gabor Comprehensive Patterns (LMGCP) was proposed. The motivation for the LMGCP model is to find more rich and discriminant texture measurement and deal with the high dimension problem of the local Gabor feature vector. First, the sample image was filtered by multi-orientation Gabor filters with multi-scale to extract their corresponding local Gabor magnitude map (LGMM). Second, the Local Comprehensive Patterns (LCP) were presented, which can compute the relationship between the referenced pixel and its neighbors by encoding gray-level difference based on 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315° high orders direction derivatives patterns (DDP) and the direction magnitude patterns (DMP). Finally, the dimension of local Gabor feature vector is higher, so the LGMM were transformed by LCP named by LMGCP, which were concatenated into and feature vector. Simulated experiments and comparisons on subsets of Yale B and CMUPIE face databases under ideal condition, different illumination condition, different facial expression and partial occlusion show that the proposed algorithm is an outstanding method better than the LBP, the local derivative patterns, and the LTP, Local Gabor Transform.

Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

Face recognition is one of the most active areas of research in computer vision. Gabor features have been used widely in face identification because of their good results and robustness. However, the results of face identification strongly depend on how different are the test and gallery images, as is the case in varying face pose. In this paper, a new Gabor-based method is proposed which modifies the grid from which the Gabor features are extracted using a mesh to model face deformations produced by varying pose. Also, a statistical model of the scores computed by using the Gabor features is used to improve recognition performance across pose. Our method incorporates blocks for illumination compensation by a Local Normalization method, and entropy weighted Gabor features to emphasize those features that improve proper identification. The method was tested on the FERET and CMU-PIE databases. Our literature review focused on articles with face identification with wide pose variation. Our results, compared to those of the literature review, achieved the highest classification accuracy on the FERET database with 2D face recognition methods. The performance obtained in the CMU-PIE database is among those obtained by the best methods published.

A New Scene Classification Method Based on Local Gabor Features

A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

Abstract Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

Supportive automatic annotation of early esophageal cancer using local gabor and color features

Over the past years High Definition (HD) endoscopy has become a crucial tool for the early detection of esophageal cancer. The high resolution offers specialist physicians high-quality visual information, enabling them to identify dysplastic tissue leading to Early Adenocarcinoma (EAC). The detection and removal of these early types of cancer drastically increases the survival chances of the patient. However, even for an experienced specialist it remains an arduous task to identify the patterns associated with early cancer. Therefore, a computer-aided detection system that supports the physician seems highly attractive. We present a novel algorithm for automatic detection of early cancerous tissue in HD endoscopic images. The algorithm computes local color- and texture features based on the original and on the Gabor-filtered image. We explore the spectral characteristics of the image regions that contain early cancer and we design appropriate filters based on this analysis. The features are classified by a trained Support Vector Machine (SVM) after which additional post-processing techniques are applied in order to annotate the image region containing early cancer. For 7 patients, we compare 32 annotations made by the algorithm with the corresponding delineations made by an expert gastroenterologist. Of 38 lesions indicated independently by the gastroenterologist, the system detects 36 of those lesions with a recall of 0.95 and a precision of 0.75.

Face recognition with adaptive local-Gabor features based on energy

Face recognition with adaptive local-Gabor features based on energy

Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

Primarily motivated by some characteristics of the human visual cortex (HVC), we propose a new facial expression recognition scheme, involving a statistical synthesis of hierarchical classifiers. In this scheme, the input images of the database are first subjected to local, multi-scale Gabor-filter operations, and then the resulting Gabor decompositions are encoded using radial grids, imitating the topographical map-structure of the HVC. The codes are fed to local classifiers to produce global features, representing facial expressions. Experimental results show that such a hybrid combination of the HVC structure with a hierarchical classifier significantly improves expression recognition accuracy when applied to wide-ranging databases in comparison with the results in the literature. Furthermore, the proposed system is not only robust to corrupted data and missing information, but can also be generalized to cross-database expression recognition.