Weber Local Descriptor Research Articles

A novel face recognition method based on IWLD and IWBC

This paper presents a face recognition method using improved Weber local descriptor (IWLD) and improved Weber binary coding method. Compared to the existing Weber local descriptor, the proposed IWLD represent local patterns more effectively and accurately by introducing novel Weber magnitude and orientation components. In order to extract more discriminative and robust feature for face recognition, the IWBC is proposed to encode the cues embedded in IWLD. Moreover, to reduce the dimension of feature extracted by IWBC and enhance its discriminative ability, the block-based Fishers linear discriminant (BFLD) is employed to learn a projection matrix from the training set. Experimental results on three (AR, FERET and PolyU-NIR) challenging databases demonstrate the effectiveness and robustness of our proposed method.

Pose-invariant face recognition using facial landmarks and Weber local descriptor

Face recognition across pose is a popular issue in biometrics. Facial rotations caused by pose dramatically enlarge the intra-class variations, which considerably obstructs the performance of the face recognition algorithms. It is advisable to extract more discriminative features to overcome this difficulty. In this paper, we present a simple but efficient feature extraction method based on facial landmarks and multi-scale fusion features. We first extract local features by using Weber local descriptors (WLD) and multi-scale patches centered at predefined facial landmarks, and then construct fusion features by randomly selecting parts of local features. Finally, the classification result is obtained by decision fusion of all local features and fusion features. The proposed method has the following two characteristics: (1) local features around landmarks can well describe the similarity between two images under pose variations and simultaneously reduce redundant information and (2) fusion features constructed by randomly selecting local features from predefined regions further alleviate the influence of pose variations. Extensive experimental results on public face datasets have shown that the proposed method greatly outperforms the previous state-of-the-art algorithms.

Date fruits classification using texture descriptors and shape-size features

In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of local binary pattern (LBP) or Weber local descriptor (WLD) histogram is applied to each of the components to encode the texture pattern of the date. The texture patterns from all the components are fused to describe the image. Fisher discrimination ratio (FDR) based feature selection is utilized to reduce the dimensionality of the feature set. Size and shape features are appended to the texture descriptors to fully describe the date. As a classifier, we use support vector machines. The proposed system achieves more than 98% accuracy to classify the dates.

High-order statistics of weber local descriptors for image representation.

Highly discriminant visual features play a key role in different image classification applications. This study aims to realize a method for extracting highly-discriminant features from images by exploring a robust local descriptor inspired by Weber's law. The investigated local descriptor is based on the fact that human perception for distinguishing a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. Therefore, we firstly transform the original stimulus (the images in our study) into a differential excitation-domain according to Weber's law, and then explore a local patch, called micro-Texton, in the transformed domain as Weber local descriptor (WLD). Furthermore, we propose to employ a parametric probability process to model the Weber local descriptors, and extract the higher-order statistics to the model parameters for image representation. The proposed strategy can adaptively characterize the WLD space using generative probability model, and then learn the parameters for better fitting the training space, which would lead to more discriminant representation for images. In order to validate the efficiency of the proposed strategy, we apply three different image classification applications including texture, food images and HEp-2 cell pattern recognition, which validates that our proposed strategy has advantages over the state-of-the-art approaches.

Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features

Vessel segmentation is an important problem in medical image analysis and is often challenging due to large variations in vessel appearance and profiles, as well as image noises. To address these challenges, we propose a solution by combining heterogeneous context-aware features with a discriminative learning framework. Our solution is characterized by three key ingredients: First, we design a hybrid feature pool containing recently invented descriptors including the stroke width transform (SWT) and Weber's local descriptors (WLD), as well as classical local features including intensity values, Gabor responses and vesselness measurements. Second, we encode context information by sampling the hybrid features from an orientation invariant local context. Third, we treat pixel-level vessel segmentation as a discriminative classification problem, and use a random forest to fuse the rich information encoded in the hybrid context-aware features. For evaluation, the proposed method is applied to retinal vessel segmentation using three publicly available benchmark datasets. On the DRIVE and STARE datasets, our approach achieves average classification accuracies of 0.9474 and 0.9633, respectively. On the high-resolution dataset HRFID, our approach achieves average classification accuracies of 0.9647, 0.9561 and 0.9634 on three different categories, respectively. Experiments are also conducted to validate the superiority of hybrid feature fusion over each individual component.

Robust face recognition using computationally efficient features

Face recognition has received enormous fame in the field of pattern recognition and computer vision. Being a demanding area intensive research has been done by many researchers for more than a decade. However no standard technique exists for extracting the significant features of facial images in different categories. Techniques found in literature produces high accuracy but are computationally expensive which are not applicable in real time applications. In this paper, two well known methods, Discrete Wavelet Transform (DWT) and Weber Local Descriptor (WLD) are used to extract the face discriminative features. First for both types of features, the recognition accuracy is separately measured. In the next step, both types of features are fused using the concatenation method to improve the accuracy rate. To select more discriminative features and reduce data dimensions, computationally efficient algorithm (Kruskal-Wallis) is used. In the last step, three classifiers (SVM, KNN and BPNN) ensemble to improve the accuracy rate. Proposed technique is more efficient in terms of time complexity as compared to GA and PSO. Yale face database is used for all experiments. The proposed technique is highly robust to facial variations like occlusion, illumination and expression change and computationally efficient as compared to existing methods.

Anisotropic diffusion algorithm based on weber local descriptor for illumination invariant face verification

The anisotropic diffusion (AD) algorithm is well known for the illumination invariant feature extraction of face images. The performance of anisotropic diffusion algorithm depends on its conduction function and discontinuity measure. However, in the traditional anisotropic diffusion algorithm, the discontinuity measure usually adopts space gradient or in-homogeneity. Though they are good at describing the local variation of image well, they cannot reflect the variation relative to its background. The relative variations tend to be more able to reflect the variation's degree of local image. In this paper, we propose an improved anisotropic diffusion algorithm that uses Weber local descriptor (WLD), a powerful and robust local descriptor as the discontinuity measure. Then, we introduce a centre-symmetric logarithmic transformation to eliminate the effect of shadow boundary. Experiments are executed in our proposed illumination invariant face verification scheme on CMU PIE, CAS-PEAL databases and a self-built real-life face database. The results demonstrate that the proposed method outperforms some typical methods on the face databases with large illumination variations.

Ratio-Detector-Based Feature Extraction for Very High Resolution SAR Image Patch Indexing

With the advent of very high resolution (VHR) synthetic aperture radar (SAR) images, local content description is becoming a critical issue for indexing. Conventional SAR image analysis techniques, like segmentation and pixel-level classification, are likely to fail as high-level semantic description should be considered for better discrimination. Therefore, we propose to use image-patch-based analysis method for SAR image interpretation. Inspired by ratio edge detector, in this letter, a new feature extraction method represented by the mean ratios in different directions is proposed for VHR SAR image content characterization. Based on the mean ratio, two simple yet powerful and robust features are proposed for SAR image patch indexing. One is the bag-of-word model using not only the basic statistics, i.e., local mean and variance, but also the mean ratios in different directions. The second one is an adaptation of the Weber local descriptor to SAR images by substituting the gradient with the ratio of mean differences in vertical and horizontal directions. To evaluate the proposed features, image patch indexing based on active learning using a SAR image database consisting of high-resolution TerraSAR-X patches is performed. Comparison with the state-of-the-art features, particularly texture features, has shown improved performance for SAR image categorization.

Face recognition using Weber local descriptors

This paper presents a method for face recognition using multi-scale Weber local descriptors (WLDs) and multi-level information fusion. Our method introduces the WLD, a novel and robust local descriptor, to describe the facial images and modifies it by a non-linear quantization approach to enhance its discriminative power. Moreover, a multi-scale framework for WLD extraction with multi-level information fusion approaches is provided for face representation and recognition. The proposed method has four main steps: (1) image partition: under given rules, each facial image is uniformly divided into a set of non-overlapped sub-regions; in this way, for a set of facial images, we therefore have a large pool of this type of sub-regions; (2) feature extraction: in this pool of sub-regions, taking one sub-region as a center, a group of similar ones are chosen for extraction of WLD histogram features; (3) features measurement: these WLD histograms are then fused into a single vector – as the feature of the center sub-region. Nearest neighborhood on chi-square is employed for similarity measurement between two sub-regions; and (4) voting: the recognition result of the entire probe (a face in sub-regions) is obtained via a voting function on the recognition result of all its sub-regions. Experimental results demonstrate the effectiveness of the proposed method upon three popular datasets.

Two Phase Non-Rigid Multi-Modal Image Registration Using Weber Local Descriptor-Based Similarity Metrics and Normalized Mutual Information

Non-rigid multi-modal image registration plays an important role in medical image processing and analysis. Existing image registration methods based on similarity metrics such as mutual information (MI) and sum of squared differences (SSD) cannot achieve either high registration accuracy or high registration efficiency. To address this problem, we propose a novel two phase non-rigid multi-modal image registration method by combining Weber local descriptor (WLD) based similarity metrics with the normalized mutual information (NMI) using the diffeomorphic free-form deformation (FFD) model. The first phase aims at recovering the large deformation component using the WLD based non-local SSD (wldNSSD) or weighted structural similarity (wldWSSIM). Based on the output of the former phase, the second phase is focused on getting accurate transformation parameters related to the small deformation using the NMI. Extensive experiments on T1, T2 and PD weighted MR images demonstrate that the proposed wldNSSD-NMI or wldWSSIM-NMI method outperforms the registration methods based on the NMI, the conditional mutual information (CMI), the SSD on entropy images (ESSD) and the ESSD-NMI in terms of registration accuracy and computation efficiency.

Learning realistic facial expressions from web images

A large amount of labeled training data is required to develop effective and robust facial expression analysis methods. However, obtaining such data is typically a tedious and time-consuming task. With a rapid advance of the Internet and Web technologies, it has been feasible to collect a large number of images with label information at a low cost of human efforts. In this paper, we propose a search based framework to collect realistic facial expression images from the Web so as to further advance research on robust facial expression recognition. Due to the limitation of current commercial web search engines, a large fraction of returned images is not related to a given query keyword. We present a Support Vector Machine (SVM) based active learning approach for selecting relevant images from noisy image search results. The resulting dataset is more diverse with more sample images per expression compared to other well established facial expression datasets such as CK and JAFFE. In addition, a novel facial expression feature based on the state-of-the-art Weber Local Descriptor (WLD) and histogram contextualization is proposed to handle such a challenging dataset. Comprehensive experimental results demonstrate that our web based dataset is capable of resembling more closely to the real world conditions compared to the CK and JAFFE datasets, and our proposed feature is more effective than the existing widely used features.

Long Range Cross-Spectral Face Recognition: Matching SWIR Against Visible Light Images

Short wave infrared (SWIR) is an emerging imaging modality in surveillance applications. It is able to capture clear long range images of a subject in harsh atmospheric conditions and at night time. However, matching SWIR images against a gallery of color images is a very challenging task. The photometric properties of images in these two spectral bands are highly distinct. This work presents a novel cross-spectral face recognition scheme that encodes images filtered with a bank of Gabor filters followed by three local operators: Simplified Weber Local Descriptor, Local Binary Pattern, and Generalized Local Binary Pattern. Both magnitude and phase of filtered images are encoded. Matching encoded face images is performed by using a symmetric I-divergence. We quantify the verification and identification performance of the cross-spectral matcher on two multispectral face datasets. In the first dataset (PRE-TINDERS), both SWIR and visible gallery images are captured at a close distance (about 2 meters). In the second dataset (TINDERS), the probe SWIR images are collected at longer ranges (50 and 106 meters). The results on PRE-TINDERS dataset form a baseline for matching long range data. We also demonstrate the capability of the proposed approach by comparing its performance with the performance of Faceit G8, a commercial face recognition engine distributed by L1. The results show that the designed method outperforms Faceit G8 in terms of verification and identification rates on both datasets.

RACE CLASSIFICATION FROM FACE IMAGES USING LOCAL DESCRIPTORS

This paper investigates and compares the performance of local descriptors for race classification from face images. Two powerful types of local descriptors have been considered in this study: Local Binary Patterns (LBP) and Weber Local Descriptors (WLD). First, we investigate the performance of LBP and WLD separately and experiment with different parameter values to optimize race classification. Second, we apply the Kruskal-Wallis feature selection algorithm to select a subset of more "discriminative" bins from the LBP and WLD histograms. Finally, we fuse LBP and WLD, both at the feature and score levels, to further improve race classification accuracy. For classification, we have considered the minimum distance classifier and experimented with three distance measures: City-block, Euclidean, and Chi-square. We have performed extensive experiments and comparisons using five race groups from the FERET database. Our experimental results indicate that (i) using the Kruskal-Wallis feature selection, (ii) fusing LBP with WLD at the feature level, and (iii) using the City-block distance for classification, outperforms LBP and WLD alone as well as methods based on holistic features such as Principal Component Analysis (PCA) and LBP or WLD (i.e., applied globally).

Memetically Optimized MCWLD for Matching Sketches With Digital Face Images

One of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.

Local Energy Pattern for Texture Classification Using Self-Adaptive Quantization Thresholds

Local energy pattern, a statistical histogram-based representation, is proposed for texture classification. First, we use normalized local-oriented energies to generate local feature vectors, which describe the local structures distinctively and are less sensitive to imaging conditions. Then, each local feature vector is quantized by self-adaptive quantization thresholds determined in the learning stage using histogram specification, and the quantized local feature vector is transformed to a number by N-nary coding, which helps to preserve more structure information during vector quantization. Finally, the frequency histogram is used as the representation feature. The performance is benchmarked by material categorization on KTH-TIPS and KTH-TIPS2-a databases. Our method is compared with typical statistical approaches, such as basic image features, local binary pattern (LBP), local ternary pattern, completed LBP, Weber local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint). The results show that our method is superior to other methods on the KTH-TIPS2-a database, and achieving competitive performance on the KTH-TIPS database. Furthermore, we extend the representation from static image to dynamic texture, and achieve favorable recognition results on the University of California at Los Angeles (UCLA) dynamic texture database.

Performance analysis of various local and global shape descriptors for image retrieval

In this paper, various prominent local and global descriptors are evaluated against each other for analyzing their performance on shape-based image retrieval. Local descriptors include Fourier descriptors, Weber's local descriptor, local binary patterns, and local ternary patterns. The prominent global descriptors include moment invariants, generic Fourier descriptor (GFD), angular radial transform (ART), wavelet moments (WM), and Zernike moment descriptor (ZMD). In addition, a novel local descriptor is proposed based on the histograms of circular arcs and linear edges, which are detected by means of Hough transform. The proposed local descriptor provides features, which are invariant to geometric transformations and are robust to noise as compared to some existing prominent local descriptors. We also propose an improvement in the performance of global descriptors GFD, ART, WM, and ZMD by taking advantage of the phase information in the comparison process along with their magnitude. Subsequently, the local and global descriptors with the best image-retrieval performances are combined to design an effective retrieval system, which further enhances the retrieval performance substantially. All descriptors are analyzed in terms of six principles set by MPEG-7. Detailed experiments are performed on standard benchmark image databases along with their rotation-invariance and noise test. The results of experiments reveal that the proposed fusion of local and global descriptors outperforms other major descriptors.

WLD: A Robust Local Image Descriptor

Inspired by Weber's Law, this paper proposes a simple, yet very powerful and robust local descriptor, called the Weber Local Descriptor (WLD). It is based on the fact that human perception of a pattern depends not only on the change of a stimulus (such as sound, lighting) but also on the original intensity of the stimulus. Specifically, WLD consists of two components: differential excitation and orientation. The differential excitation component is a function of the ratio between two terms: One is the relative intensity differences of a current pixel against its neighbors, the other is the intensity of the current pixel. The orientation component is the gradient orientation of the current pixel. For a given image, we use the two components to construct a concatenated WLD histogram. Experimental results on the Brodatz and KTH-TIPS2-a texture databases show that WLD impressively outperforms the other widely used descriptors (e.g., Gabor and SIFT). In addition, experimental results on human face detection also show a promising performance comparable to the best known results on the MIT+CMU frontal face test set, the AR face data set, and the CMU profile test set.