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105
Fisher Discrimination Dictionary Learning for Sparse Representation
"... Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimi ..."
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Cited by 73 (9 self)
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Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.
Partial Face Recognition: Alignment-Free Approach
"... Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld ..."
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Cited by 20 (8 self)
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Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld devices (e.g. mobile phones) in particular. In this paper, we propose a general partial face recognition approach that does not require face alignment by eye coordinates or any other fiducial points. We develop an alignment-free face representation method based on Multi-Keypoint Descriptors (MKD), where the descriptor size of a face is determined by the actual content of the image. In this way, any probe face image, holistic or partial, can be sparsely represented by a large dictionary of gallery descriptors. A new keypoint descriptor called Gabor Ternary Pattern (GTP) is also developed for robust and discriminative face recognition. Experimental results are reported on four public domain face databases (FRGCv2.0, AR, LFW, and PubFig) under both the open-set identification and verification scenarios. Comparisons with two leading commercial face recognition SDKs (PittPatt and FaceVACS) and two baseline algorithms (PCA+LDA and LBP) show that the proposed method, overall, is superior in recognizing both holistic and partial faces without requiring alignment.
Relaxed Collaborative Representation for Pattern Classification
"... Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representati ..."
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Cited by 19 (0 self)
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Regularized linear representation learning has led to interesting results in image classification, while how the object should be represented is a critical issue to be investigated. Considering the fact that the different features in a sample should contribute differently to the pattern representation and classification, in this paper we present a novel relaxed collaborative representation (RCR) model to effectively exploit the similarity and distinctiveness of features. In RCR, each feature vector is coded on its associated dictionary to allow flexibility of feature coding, while the variance of coding vectors is minimized to address the similarity among features. In addition, the distinctiveness of different features is exploited by weighting its distance to other features in the coding domain. The proposed RCR is simple, while our extensive experimental results on benchmark image databases (e.g., various face and flower databases) show that it is very competitive with state-of-the-art image classification methods. 1.
Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization
"... Abstract. Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representatio ..."
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Cited by 17 (1 self)
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Abstract. Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a nontrivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multiscale patchbasedCRCmethod,while theensembleofmulti-scaleoutputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms. 1
Sparse Representation with Kernels
, 2012
"... Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps find a sparse representation of nonlinear features, we therefore propose Kernel Sparse Repr ..."
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Cited by 12 (3 self)
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Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps find a sparse representation of nonlinear features, we therefore propose Kernel Sparse Representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition and kernel matrix approximation. More specifically, by incorporating KSR into Spatial Pyramid Matching (SPM), we develop KSRSPM, which achieves good performance for image classification. Moreover, KSR based feature coding can be shown as a generalization of Efficient Match Kernel (EMK) and an extension of Sc based SPM (ScSPM). We further show that our proposed KSR using Histogram Intersection Kernel (HIK) can be considered as a soft assignment extension of HIK based feature quantization in feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in the image classification, face recognition and kernel matrix approximation. All these applications proves the effectiveness of KSR in computer vision and machine learning tasks.
Nearest Regularized Subspace for Hyperspectral Classification
- IEEE Transactions on Geoscience and Remote Sensing, Submitted April 2012. Revised
, 2012
"... Abstract—A classifier that couples nearest-subspace classification with a distance-weighted Tikhonov regularization is proposed for hyperspectral imagery. The resulting nearest-regularizedsubspace classifier seeks an approximation of each testing sample via a linear combination of training samples w ..."
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Cited by 11 (10 self)
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Abstract—A classifier that couples nearest-subspace classification with a distance-weighted Tikhonov regularization is proposed for hyperspectral imagery. The resulting nearest-regularizedsubspace classifier seeks an approximation of each testing sample via a linear combination of training samples within each class. The class label is then derived according to the class which best approximates the test sample. The distance-weighted Tikhonov regularization is then modified by measuring distance within a locality-preserving lower-dimensional subspace. Furthermore, a competitive process among the classes is proposed to simplify parameter tuning. Classification results for several hyperspectral image data sets demonstrate superior performance of the proposed approach when compared to other, more traditional classification techniques. Index Terms—Classification, hyperspectral data, Tikhonov regularization. I.
Robust and Practical Face Recognition via Structured Sparsity
"... Abstract. Sparse representation based classification (SRC) methods have recently drawn much attention in face recognition, due to their good performance and robustness against misalignment, illumination variation, and occlusion. They assume the errors caused by image variations can be modeled as pix ..."
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Cited by 11 (0 self)
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Abstract. Sparse representation based classification (SRC) methods have recently drawn much attention in face recognition, due to their good performance and robustness against misalignment, illumination variation, and occlusion. They assume the errors caused by image variations can be modeled as pixel-wisely sparse. However, in many practical scenarios these errors are not truly pixel-wisely sparse but rather sparsely distributed with structures, i.e., they constitute contiguous regions distributed at different face positions. In this paper, we introduce a class of structured sparsity-inducing norms into the SRC framework, to model various corruptions in face images caused by misalignment, shadow (due to illumination change), and occlusion. For practical face recognition, we develop an automatic face alignment method based on minimizing the structured sparsity norm. Experiments on benchmark face datasets show improved performance over SRC and other alternative methods. 1
Regularized Robust Coding for Face Recognition
"... Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. ..."
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Cited by 11 (3 self)
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Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes SRC’s computational cost very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR 3 C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting and expression changes, etc.
Iterative Nearest Neighbors for Classification and Dimensionality Reduction
"... Representative data in terms of a set of selected sam-ples is of interest for various machine learning applica-tions, e.g. dimensionality reduction and classification. The best-known techniques probably still are k-Nearest Neigh-bors (kNN) and its variants. Recently, richer representa-tions have bec ..."
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Cited by 9 (8 self)
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Representative data in terms of a set of selected sam-ples is of interest for various machine learning applica-tions, e.g. dimensionality reduction and classification. The best-known techniques probably still are k-Nearest Neigh-bors (kNN) and its variants. Recently, richer representa-tions have become popular. Examples are methods based on l1-regularized least squares (Sparse Representation (SR)) or l2-regularized least squares (Collaborative Representa-tion (CR)), or on l1-constrained least squares (Local Linear Embedding (LLE)). We propose Iterative Nearest Neighbors (INN). This is a novel sparse representation that combines the power of SR and LLE with the computational simplicity of kNN. We test our method in terms of dimensionality re-duction and classification, using standard benchmarks such as faces (AR), traffic signs (GTSRB), and PASCAL VOC 2007. INN performs better than NN and comparable with CR and SR, while being orders of magnitude faster than the latter. 1.
Collaborative Representation based Classification for Face Recognition
"... By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed t ..."
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Cited by 8 (5 self)
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By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.
