Deep Sparse Representation-Based Classification

In this paper, we present a facial expression recognition method based on Gabor feature and sparse representation. Sparse Representation based Classification (SRC) has been widely used in computer vision and pattern recognition. And Gabor filter banks can be used to approximately model the signal processing in visual primary cortex. We believe that the nature of the attractive performance of SRC and Gabor feature lies in that they both followed the natures of signal perception of retina and information processing of cortex in human vision. Therefore, we combined the Gabor feature and SRC for facial expression recognition. The comparison experiments of proposed Gabor+SRC algorithm and straightforward SRC application are conducted on JAFFE database. And the experimental results showed the attractive performance of the proposed Gabor+SRC method.

Sparse representation based classification (SRC) has gained great success in image recognition. In this paper, we propose a generalized CRC method, essentially a SRC using Euler sparse representation, for image classification. To be specific, our CRC first maps the image into a complex space by Euler representation with a negligible effect for outliers and illumination, and then performs a complex SRC method. We also present a fast and efficient algorithm to solve Euler SRC subproblems. Extensive experimental results illustrate that our method outperforms traditional SRC methods and achieves better performance for image classification.

Sparse representation based classification (SRC) and collaborative representation based classification (CRC) are two well-known methods in representation-based classification for face recognition. SRC emphasizes on the role of sparsity in coding, while CRC obtains an enhanced sparse representation with collaborative contributions from all classes. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for robust classification that may amplify the nonlocal contribution from other classes. However, the parameters are closely associated with the sparsity and it is very difficult to solve the optimal solutions. This paper proposed a novel representation-based image classification method that integrates SRC with CRC by a multiplication operation on the solutions to obtain enhanced sparse representation. We conducted experiments on several benchmark face databases and the experimental results showed that our algorithm produces higher accuracy on classification than both SRC and CRC.

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.

In recent years, sparse representation and dictionary learning methods have produced state-of-the-art results in many biometric recognition problems such as face, gait and iris recognition. However, when sparse representation-based classification methods are confronted with situations where the training data has different distribution than the test data, their performance degrades significantly. In this paper, we propose a general sparse representation-based classification method that learns projections of data in a space where the sparsity of data is maintained. We propose an efficient iterative procedure for solving the proposed optimization problem. One of the key features of the proposed method is that it is computationally efficient as the learning is done in the lower-dimensional space. Various experiments on mobile active authentication datasets consisting of face and screen touch gestures show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive domain adaptation algorithms.

This paper presents a method to perform sparse representation based classification (SRC) in a more accurate and efficient way. In this method, training data is first mapped into different feature spaces and multiple dictionaries are built by utilizing a Fisher discriminative based method. These dictionaries can be considered as efficient representations of the data which are then used in a multimodal SRC framework to classify test samples. In comparison to the original SRC method where only one modality of training space is utilized, the proposed method classifies test samples in a more accurate and efficient way. Experimental results from two different face datasets show that the proposed multimodal method has higher recognition rate compared to single-modality SRC based methods. The accuracy of the proposed method is also compared to other multi-modality classifiers and the results confirm that higher recognition rates are achieved in comparison with other common classification algorithms.

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structureRSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals aggregation. In addition, three strategies, local-neighbourhood representation local intra-class Bayesian residual criterion and local auxiliary dictionary are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

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.

Robust automatic facial expression detection method based on sparse representation plus LBP map

Aiming at the limitation of single feature classification, this paper proposes a facial expression recognition algorithm based on feature fusion and sparse representation residual fusion. The proposed method is composed of three components. Firstly, a feature fusion method based on local binary quantization (LBP) and local phase quantization (LPQ) is proposed. The LBP/LPQ features are an expression feature with image space and frequency domain. Second, Curvelet transform is used to extract the global features of facial expression, which is a very effective multi-scale, band-pass and direction analysis tool. Finally, the LBP/LPQ features and the Curvelet feature are classified by the sparse representation based classification (SRC) with stronger discrimination and robustness respectively. According to the sparse residuals of LBP/LPQ+SRC and Curvelet+SRC, a classification method of residual fusion is proposed. Experiments on JAFFE database and Cohn-Kanade database show that the proposed method effectively improves the ability of facial expression recognition and achieves a high recognition rate.

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

Recently, the sparse representation based classification methods have received particular attention in the classification of hyperspectral imagery. However, current sparse representation based classification models have not considered all the test pixels simultaneously. In this paper, we propose a hyperspectral classification method with spatial filtering and norm (SFL) that can deal with all the test pixels simultaneously. The norm regularization is used to extract relevant training samples among the whole training data set with joint sparsity. In addition, the norm loss function is adopted to make it robust for samples that deviate significantly from the rest of the samples. Moreover, to take the spatial information into consideration, a spatial filtering step is implemented where all the training and testing samples are spatially averaged with its nearest neighbors. Furthermore, the non-negative constraint is added to the sparse representation matrix motivated by hyperspectral unmixing. Finally, the alternating direction method of multipliers is used to solve SFL. Experiments on real hyperspectral images demonstrate that the proposed SFL method can obtain better classification performance than some other popular classifiers.

Recognition of emotional content in music is an issue that arises recently. Music received by live applications are often exposed to noise, thus prone to reducing the recognition rate of the application. The solution proposed in this study is a robust music emotion recognition system for live applications. The proposed system consists of two major parts, i.e. subspace-based noise suppression and a hierarchical sparse representation classifier, which is based on sparse coding and a sparse representation classifier (SRC). The music is firstly enhanced by fast subspace based noise suppression. Nine classes of emotion are then used to construct a dictionary, and the vector of coefficients is obtained by sparse coding. The vector can be divided into nine parts, and each of which models a specific emotional class of a signal. Since the proposed descriptor can provide emotional content analysis of different resolutions for emotional music recognition, this work regards vectors of coefficients as feature representations. Finally, a sparse representation based classification method is employed for classification of music into four emotional classes. The experimental results confirm the highly robust performance of the proposed system in emotion recognition in live music.

During the last decade, sparse representations have been successfully applied to design high-performing classification algorithms such as the classical sparse representation based classification (SRC) algorithm. More recently, collaborative representation based classification (CRC) has emerged as a very powerful approach, especially for face recognition. CRC takes advantage of SRC through the notion of collaborative representation, relying on the observation that the collaborative property is more crucial for classification than the l1-norm sparsity constraint on coding coefficients used in SRC. This paper follows the same general philosophy of CRC and its main novelty is the application of a virtual collaborative projection (VCP) routine designed to train images of every class against the other classes to improve fidelity before the projection of the query image. We combine this routine with a method of local feature extraction based on high-order statistical moments to further improve the representation. We demonstrate using extensive experiments of face recognition and classification that our approach performs very competitively with respect to state-of-the-art classification methods. For instance, using the AR face dataset, our method reaches 100% of accuracy for dimensionality 300.

The sparse representation based classification (SRC) can effectively improve the face recognition rate. Smoothed l 0 algorithm has much faster calculation speed and requires fewer measured values than the other sparse representation method. In this paper, the sparse representation and smoothed l 0 algorithm for face recognition are presented to improve the face recognition under various conditions such as face disguise, illumination and pose changes, etc. The experiments on the AR, Extended Yale B and FERET face database verify the effectiveness of the presented method. The experimental results show that the face recognition algorithm increases to a certain extent in terms of recognition robustness and time than the original SRC.