Robust face recognition based on sparse representation in 2D Fisherface space

Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness.     

A novel virtual samples-based sparse representation method for face recognition

A limited training set usually limits the performance of face recognition in practice. Even sparse representation-based methods which outperform in face recognition cannot avoid such situation. In order to effectively improve recognition accuracy of sparse representation-based methods on a limited training set, a novel virtual samples-based sparse representation (VSSR) method for face recognition is proposed in this paper. In the proposed method, virtual training samples are constructed to enrich the size and diversity of a training set and a sparse representation-based method is used to classify test samples. Extensive experiments on different face databases confirm that VSSR is robust to illumination variations and works better than many representative representation-based face recognition methods.     

Concentrative sparse representation based classification

Sparse representation is being proved to be effective for many tasks in the field of pattern recognition. In this paper, an efficient classification algorithm based on concentrative sparse representation will be proposed to address the problem caused by insufficient training samples in each class. We firstly compute representation coefficient of the testing sample with training samples matrix using subspace pursuit recovery algorithm. Then we define concentration measurement function in order to determine whether the sparse representation coefficient is concentrative. Subspace pursuit is repeatedly used to revise the sparse representation until concentration is met. Such a concentrative sparse representation can contribute to discriminative residuals that are critical to accurate classification. The experimental results have showed that the proposed algorithm achieves a satisfying performance in both accuracy and efficiency.     

Visual attention guided image fusion with sparse representation

Image fusion techniques aim at transferring useful information from the input source images to the fused image. The common assumption for most fusion approaches is that the useful information is defined by local features such as contrast, variance, and gradient. However, there is no consideration of global visual attention of the whole source images which indicates the “interesting” information of the source images. In this paper, we firstly review the patch-based image fusion methods which attract the attention and interest of many researchers. Then, a visual attention guided patch-based image fusion method is proposed. The visual attention maps of the source images are calculated from the sparse represent coefficients of the source images. Then, the sparse coefficients are fused with the guidance of visual attention maps in order to emphasize the global “interesting” objects in the source images. Finally, the fused image is reconstructed from the fused sparse coefficients. The new fusion strategy ensures that the objects being “interesting” for our visual system are preserved in the fused image. The proposed approach is tested on infrared and visual, medical, and multi-focus images. The results compared with those of traditional methods show obvious improvement in objective and subjective quality measurements.     

A sparse representation method based on kernel and virtual samples for face recognition

To improve the classification accuracy of face recognition, a sparse representation method based on kernel and virtual samples is proposed in this paper. The proposed method has the following basic idea: first, it extends the training samples by copying the left side of the original training samples to the right side to form virtual training samples. Then the virtual training samples and the original training samples make up a new training set and we use a kernel-induced distance to determine M nearest neighbors of the test sample from the new training set. Second, it expresses the test sample as a linear combination of the selected M nearest training samples and finally exploits the determined linear combination to perform classification of the test sample. A large number of face recognition experiments on different face databases illustrate that the error ratios obtained by our method are always lower more or less than face recognition methods including the method mentioned in Xu and Zhu [21], the method proposed in Xu and Zhu [39], sparse representation method based on virtual samples (SRMVS), collaborative representation based classification with regularized least square (CRC_RLS), two-phase test sample sparse representation (TPTSSR), and the feature space-based representation method.     

A novel method for recognizing face with partial occlusion via sparse representation

In this paper, we propose a novel method to recognize the partially occluded face images based on sparse representation. Generally, occlusions, such as glasses and scarf, fall on some random patch of image's pixels of test images, but which is known to be connected. In our method, all images are divided into several blocks and then an indicator based on linear regression technique is presented to determine whether a block is occluded. We utilize those uncontaminated blocks as the new feature of an image. Finally, the sparse representation-based classification (SRC) method serves as the classifier to recognize unknown faces. In the original work of SRC, the extended SRC (eSRC) scheme is presented to deal with occlusions, which has very heavy computational cost. The experimental results show that our method can achieve good recognition accuracy and has much lower computational cost than eSRC.     

A face recognition algorithm based on collaborative representation

In this paper, we propose a face recognition algorithm by incorporating a neighbor matrix into the objective function of sparse coding. We first calculate the neighbor matrix between the test sample and each training sample by using the revised reconstruction error of each class. Specifically, the revised reconstruction error (RRE) of each class is the division of the l₂-norm of reconstruction error to the l₂-norm of reconstruction coefficients, which can be used to increase the discrimination information for classification. Then we use the neighbor matrix and all the training samples to linearly represent the test sample. Thus, our algorithm can preserve locality and similarity information of sparse coding. The experimental results show that our algorithm achieves better performance than four previous algorithms on three face databases.     

Single-image super-resolution with joint-optimization of TV regularization and sparse representation

A super-resolution (SR) reconstruction framework is proposed using regularization restoration combined with learning-based resolution enhancement via sparse representation. With the viewpoint of conventional learning methods, the original image can be split into low frequency (LF) and high frequency (HF) components. The reconstruction mainly focuses on the process of HF part, while the LF one is founded simply by typical interpolation function. For the severely blurred single-image, we first use regularization restoration technology to recover it. Then the regularized output remarkably betters the quality of LF used in traditional learning-based methods. Last, image resolution enhancement with characteristic of edge preserving can implement based on the acquired relatively sharp intermediate image and the pre-constructed over-complete dictionary for sparse representation. Specifically, the regularization can favorably weaken the dependence of atoms on the course of degradation. With both techniques, we can noticeably eliminate the blur and the edge artifacts in the enlarged image simultaneously. Various experimental results demonstrate that the proposed approach can produce visually pleasing resolution for severely blurred image.     

The easy block Hadamard transform: A new adaptive directional Hadamard transform for sparse image representation

In order to get an efficient image representation, we introduce a new locally adaptive Hadamard transform, called Easy Block Hadamard Transform (EBHT). EBHT is a type of directional Hadamard transform, which can be adapted to the image geometry in each block. Firstly, the image is divided into eight equal-sized squares and the adaptive directional Hadamard filter is employed to get the low-pass data and high-pass data. In the further level of the filter bank algorithm, we just divide the low-pass image into four equal-sized squares. EBHT is very simple, but effective. Numerical results show the excellent efficiency of the EBHT for sparse image representation.     

Broadband underwater multi-source localization with a computationally efficient coherent OMP algorithm

In this paper, we propose a broadband coherent matched-field processing (MFP) algorithm to solve multi-source localization problems in shallow water scenarios. The proposed algorithm combines the matched-phase coherent processor with the sparse recovery technique from compressive sensing (CS) theory. A greedy sparse recovery algorithm is adopted to iteratively locate multiple sources using a matched-phase coherent processor. At each iteration of the greedy algorithm, the data is processed coherently using the phase descent search (PDS) algorithm, rather than the incoherent methods used in many sparse recovery algorithms, such as the classical orthogonal matching pursuit (OMP) algorithm. The phase shifts between different frequencies are estimated and compensated, such that the performance can be greatly enhanced. The proposed algorithm is applied to simulated data, synthesized data, and data collected in the SWellEx-96 shallow water experiment. The result provides sparse localization information that matches the ground truth source locations in the simulation and the source trajectory calculated from the Global Positioning System (GPS) information from the experiment.