Sparse Representation-based Classification Research Articles

A Discriminative Projection and Representation-Based Classification Framework for Face Recognition

The sparse representation-based classifier (SRC) has been developed and verified as having great potential for real-world face recognition. In this paper, we propose a discriminative projection and representation-based classification (DPRC) method to enhance the discriminant ability of the SRC. The proposed method first obtains a discriminative projection matrix not only maximizing the ratio of the distance within interclass over the distance within intraclass, but also minimizing the linear approximation error within intraclass. Then it maps the original data onto the discriminative space, and adopts an SRC method to obtain the final solution. An inexact augmented Lagrangian method of multiplier is proposed for finding the optimal representation vector in our framework, and a proximal alternating minimization method is adopted to the iteration subproblems of the proposed method. The proposed method is proven to have the subsequence convergence property. Experimental results on Yale, ORL, and AR face image databases demonstrate that, compared with some existing feature extraction methods based on the SRC, the proposed DPRC method is more efficient.

Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition

Sparse representation based classification method has led to a wide variety of extensions of representation based methods for face recognition. All of these methods partially reveal that collaborative representation is a crucial factor to make sparse representation based classification powerful for face recognition. The collaborative representation based classification (CRC) methods and corresponding variations have achieved effective results in face recognition. For these methods, we found that the test sample has some relevance with the coefficient vector. For example, nonzero elements in the coefficient vector are associated with the classes which the test sample potentially belong to. Exploiting the relevance may obtain sparser coefficient vector in comparison with the traditional methods. Hence, we propose a novel method in which the test sample is closely involved in the solution procedure of optimal coefficient vector. The classification of the proposed method is performed by checking the minimal residual between the test sample and the collaborative representation with respect the test sample of the selected class, which is similar to that of CRC. The proposed method can intensify the corresponding coefficients in the coefficient vector by exploiting the test sample. Experimental results show that the proposed method does achieve more accurate recognition rate.

Face Recognition Using the Combination of Weighted Sparse Representation-based Classification and Singular Value Decomposition Face

Given the increasing need for the creation and development of automated systems, the problem of detecting and identifying the faces of people in the images has been considered by the researchers. In recent years, the sparse representation based classification has been of great interest to researchers. The goal of this investigation is to provide a quick and effective way to identify faces based on the sparse representation. Since the basis of sparse representation is to calculate it through L1-norm optimization for high dimensional dictionary with high computational volume, a smoothed L0-norm optimization-based method was introduced. At the time of obtaining the sparse representation using smoothed L0-norm, due to the high degree of redundancy, when the number of facial features is low, the conditions for the unique sparse representation are not properly satisfied or the model is not accurate, the risk of getting stuck in the local minima will be so much. Hence, the idea of the weighted sparse representation to narrow the search space is presented. In addition, to enhance the identification accuracy in face recognition and face verification under varying illumination conditions, the Singular Value Decomposition method can be used in the feature extraction step. In the extraction stage, the normalized coefficients of Singular Value Decomposition are not sensitive to different lightness conditions, resulting to reduce the effect of illumination variation on the normalized images. Compared to earlier methods, which often fail even in the event of a slight turbulence, this technique can successfully produce facial structural information while preventing the effect of different lightnesses such as cast shadows. The simulation results on the Extended Yale B database show that the proposed method has high accuracy in face recognition with less computational volume and higher speed than the L1-norm based approach.

Automatic Identification of Epileptic Seizures From EEG Signals Using Sparse Representation-Based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician.

Sparse representation-based classification for the planetary gearbox with improved KPCA and dictionary learning

ABSTRACT A fault diagnosis method for the planetary gearbox according to sparse representation-based classification (SRC) has been presented in this paper. Considering the real-time performance and accuracy rate of the fault diagnosis, the proposed method has introduced the improved kernel principal component analysis (KPCA) and dictionary learning. First, some time domain and frequency domain features are combined into a feature vector to represent a sample, which can reduce the computational burden and enhance the real-time performance of fault classification. Second, the feature sets are transformed into a new feature space through the improved KPCA, which can improve the precision of fault classification. Then, the training samples are used to implement dictionary learning, and the testing samples are taken as the input of the SRC for classifying. Finally, a planetary gearbox fault diagnosis experiment is designed to verify the effectiveness of the proposed method.

Fast kernel sparse representation based classification for Undersampling problem in face recognition

We propose a fast kernel sparse representation based classification (SRC) for undersampling problem, i.e., each class has very few training samples, in face recognition. The proposed algorithm exploits a nonlinear mapping to map the data from the original input space into a high-dimensional feature space. Then, it performs very fast sparse representation and classification of samples in this space. Similar to the typical SRC methods, the proposed approach is based on the L1 norm minimization, whose direct solution can be very time-consuming. In order to improve the computational efficiency, our method uses the coordinate descent method in the feature space, which can avoid directly solving the L1 norm minimization problem, and significantly expedites the computational procedure. Compared with other SRC methods based on the L1 norm minimization, our proposed method achieves very high computational efficiency, without significantly degrading the classification performance. Several experiments on popular face databases demonstrate that our method is a promising efficient kernel SRC based method.

Learning a representation with the block-diagonal structure for pattern classification

Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns Representation with Block-Diagonal Structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method.

A novel automatic classification detection for epileptic seizure based on dictionary learning and sparse representation

Electroencephalogram (EEG) signals play an important role in the epilepsy detection. In the past decades, the automatic detection system of epilepsy has emerged and performed well. In this paper, a novel sparse representation-based epileptic seizure classification based on the dictionary learning with homotopy (DLWH) algorithm is proposed. The performance of the proposed method evaluates on two public EEG databases provided by the Bonn University and Childrens Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), various classification cases that include health and seizure; non-seizure and seizure; inter ictal (seizure-free interval) and ictal (seizure). The results show that the DLWH only completes the test with 19.671 s compared with the traditional sparse representation methods with high degree of automation, which are better than those obtained using the well-known dictionary learning method. Besides, two publicly available benchmark databases recognition rates are as high as 100%, 99.5%, with average of 99.5% and 95.06%,% respectively, and the results show that the epileptic detection system based on the dictionary learning has a high application value.

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

A paired sparse representation model for robust face recognition from a single sample

Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary dictionary. Despite their success in handling linear variations, non-linear variations (e.g., pose and expressions) between probe and reference facial images cannot be accurately reconstructed with a linear combination of images in the gallery and auxiliary dictionaries because they do not share the same type of variations. In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images. The proposed model, called synthetic plus variational model, reconstructs a probe image by jointly using (1) a variational dictionary and (2) a gallery dictionary augmented with a set of synthetic images generated over a wide diversity of pose angles. The augmented gallery dictionary is then encouraged to pair the same sparsity pattern with the variational dictionary for similar pose angles by solving a newly formulated simultaneous sparsity-based optimization problem. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a single sample per person.

Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation.

An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.

Source Microphone Recognition Aided by a Kernel-Based Projection Method

Microphone recognition aims at recognizing different microphones based on the recorded speeches. In the literature, Gaussian Supervector (GSV) has been used as the feature vector representing a speech recording, which is obtained by adapting a universal background model (UBM). However, it is not clear how the performance of the GSV will be affected by the number of mixture components in the UBM. Besides, the raw GSV obtained from a speech recording contains both the microphone response information and the speech information, meaning that the raw GSV can be quite noisy as the feature vector for microphone recognition. In this paper, we investigate how GSV will be affected by the UBM and other parameters during the calculation of the GSV. In addition, in order to improve the quality of the raw GSV, we propose a kernel-based projection method to be applied to the raw GSV. This projection method maps the raw GSV onto another dimensional space. It is expected that in the projected feature space, the microphone response information and the speech information can be separated into different dimensions, meaning that the projected GSV should be better as the feature vector for microphone recognition compared to the raw GSV. Two classifiers that have been used in the literature, namely linear support vector machine (SVM) and sparse representation-based classifier (SRC), are employed to compare the performance of the raw GSV and the projected GSV. The experimental results demonstrate that the projected GSV can outperform the raw GSV no matter using linear SVM or SRC as the classifier, which shows the effectiveness of the projection method.

Video Face Recognition Using Siamese Networks With Block-Sparsity Matching

Deep learning models for still-to-video FR typically provide a low level of accuracy because faces captured in unconstrained videos are matched against a reference gallery comprised of a single facial still per individual. For improved robustness to intra-class variations, deep Siamese networks have recently been used for pair-wise face matching. Although these networks can improve state-of-the-art accuracy, the absence of prior knowledge from the target domain means that many images must be collected to account for all possible capture conditions, which is not practical for many real-world surveillance applications. In this paper, we propose the deep SiamSRC network that employs block-sparsity for face matching, while the reference gallery is augmented with a compact set of domain-specific facial images. Prior to deployment, clustering based on row sparsity is performed on unlabelled faces captured in videos from the target domain. Cluster centers discovered in the capture condition space (defined by, e.g., pose, scale and illumination) are used as rendering parameters with an off-the-shelf 3D face model, and a compact set of synthetic faces are thereby generated for each reference still based on representative intra-class information from the target domain. For pair-wise similarity matching with query facial images, the SiamSRC exploits sparse representation-based classification with a block structure. Experimental results obtained with the videos from the Chokepoint and COX-S2V datasets indicate that the proposed SiamSRC network can outperform state-of-the-art methods for still-to-video FR with a single sample per person, with only a moderate increase in computational complexity.

Collaborative Representation and Sparsity are Both Indispensable for Hyperspectral Imagery Classification

As a recently proposed technique, sparse representation based classifier (SRC) has been widely used for hyperspectral imagery classification and detection. The collaborative representation (CR) and the sparse coding are two key points in SRC scheme. More recently, the proposition that which one of them plays a dominant role in SRC scheme has attracted much attention from researchers in fields of image processing, computer vision, and pattern recognition. In this paper, we first discuss why CR or sparsity works and why one of them alone is not sufficient, and then analyze how CR and sparsity interact with each other. Although we focus on how sparsity augments CR, the necessity of CR for sparsity is also illustrated in both pixel-wise model and joint sparsity model. Inspired by the analysis, we indicate that CR is a powerful tool for solving the high-dimensional pattern recognition with small sample in SRC scheme; sparsity augments CR-based classification in stabilizing, making sure unique solution and rejecting outlying samples. In other words, CR and sparsity complement each other and are both indispensible for hyperspectral imagery classification. The experimental results on simulated data and real hyperspectral imagery confirm the conclusion.

Deep Cascade Model based Face Recognition: When Deep-layered Learning Meets Small Data.

Sparse representation based classification (SRC), nuclear-norm matrix regression (NMR), and deep learning (DL) have achieved a great success in face recognition (FR). However, there still exist some intrinsic limitations among them. SRC and NMR based coding methods belong to one-step model, such that the latent discriminative information of the coding error vector cannot be fully exploited. DL, as a multi-step model, can learn powerful representation, but relies on large-scale data and computation resources for numerous parameters training with complicated back-propagation. Straightforward training of deep neural networks from scratch on small-scale data is almost infeasible. Therefore, in order to develop efficient algorithms that are specifically adapted for small-scale data, we propose to derive the deep models of SRC and NMR. Specifically, in this paper, we propose an end-to-end deep cascade model (DCM) based on SRC and NMR with hierarchical learning, nonlinear transformation and multi-layer structure for corrupted face recognition. The contributions include four aspects. First, an end-to-end deep cascade model for small-scale data without back-propagation is proposed. Second, a multi-level pyramid structure is integrated for local feature representation. Third, for introducing nonlinear transformation in layer-wise learning, softmax vector coding of the errors with class discrimination is proposed. Fourth, the existing representation methods can be easily integrated into our DCM framework. Experiments on a number of small-scale benchmark FR datasets demonstrate the superiority of the proposed model over state-of-the-art counterparts. Additionally, a perspective that deep-layered learning does not have to be convolutional neural network with back-propagation optimization is consolidated. The demo code is available in https://github.com/liuji93/DCM.

Non-Intrusive Load Monitoring via Multi-Label Sparse Representation-Based Classification

This work follows the approach of multi-label classification for non-intrusive load monitoring (NILM). We modify the popular sparse representation based classification (SRC) approach (developed for single label classification) to solve multi-label classification problems. Results on benchmark REDD and Pecan Street dataset shows significant improvement over state-of-the-art techniques with small volume of training data.

Hyperspectral Image Classification Using Kernel Fused Representation via a Spatial-Spectral Composite Kernel With Ideal Regularization

To adequately exploit spectral, spatial, and label information of the given hyperspectral data, a kernel fused representation-based classifier via a spatial-spectral composite kernel with ideal regularization (CKIR) method is proposed in this letter. Specifically, the learned CKIR is embedded into the kernel version of representation-based classifiers, i.e., kernel sparse representation-based classifier (KSRC) and kernel collaborative representation-based classifier (KCRC), to obtain more discriminative representation coefficients. Furthermore, to benefit from both sparsity and data correlation in representation, KSRC and KCRC are combined in the CKIR-based residual domain to further enhance the discriminative ability of the proposed classifier. The experimental results on two real hyperspectral images demonstrate that the proposed method outperforms the other state-of-the-art classifiers.

Multi-Kernel Coupled Projections for Domain Adaptive Dictionary Learning

Dictionary learning has produced state-of-the-art results in various classification tasks. However, if the training data have a different distribution than the testing data, the learned sparse representation might not be optimal. Recently, several domain-adaptive dictionary learning (DADL) methods and kernels have been proposed and have achieved impressive performance. However, the performance of these single kernel-based methods heavily depends heavily on the choice of the kernel, and the question of how to combine multiple kernel learning (MKL) with the DADL framework has not been well studied. Motivated by these concerns, in this paper, we propose a multi-kernel domain-adaptive sparse representation-based classification (MK-DASRC) and then use it as a criterion to design a multi-kernel sparse representation-based domain-adaptive discriminative projection method, in which the discriminative features of the data in the two domains are simultaneously learned with the dictionary. The purpose of this method is to maximize the between-class sparse reconstruction residuals of data from both domains, and minimize the within-class sparse reconstruction residuals of data in the low-dimensional subspace. Thus, the resulting representations can satisfactorily fit MK-DASRC and simultaneously display discriminability. Extensive experimental results on a series of benchmark databases show that our method performs better than the state-of-the-art methods.

An Object Recognition Approach for Synthetic Aperture Radar Images

In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the l1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data, but also take advantage of the polarization diversity and the aspect angle dependence information from multichannel SAR data. First, the traditional sparse representation-based classification is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Finally, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the data set we consider. Extensive experimental results on a high-fidelity electromagnetic simulated data set and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.