Jianwei Zhao

Pose and illumination variable face recognition via sparse representation and illumination dictionary

This paper addresses the problem of face recognition under pose and illumination variations, and proposes a novel algorithm inspired by the idea of sparse representation (SR). In order to make the SR early designed for the pose-invariant face recognition suitable for the case of pose variation, a multi-pose weighted sparse representation (MW-SR) algorithm is proposed to emphasize the contributions of the similar poses in the representation of the test image. Furthermore, when some illumination variations are added to the images, it is more reasonable to take advantage of the results of pose variable recognition and avoid the traditional SR method that adds all kinds of images with pose and illumination variations in the training dictionary. Here, a novel idea of the proposed algorithms is adding a general illumination dictionary to the training dictionary, and that once the illumination dictionary is designed, it is common for the other face databases. Extensive experiments illustrate that the proposed algorithms perform better than some existing methods for the face recognition under pose and illumination variations.

Recovering low-rank and sparse matrix based on the truncated nuclear norm

Recovering the low-rank, sparse components of a given matrix is a challenging problem that arises in many real applications. Existing traditional approaches aimed at solving this problem are usually recast as a general approximation problem of a low-rank matrix. These approaches are based on the nuclear norm of the matrix, and thus in practice the rank may not be well approximated. This paper presents a new approach to solve this problem that is based on a new norm of a matrix, called the truncated nuclear norm (TNN). An efficient iterative scheme developed under the linearized alternating direction method multiple framework is proposed, where two novel iterative algorithms are designed to recover the sparse and low-rank components of matrix. More importantly, the convergence of the linearized alternating direction method multiple on our matrix recovering model is discussed and proved mathematically. To validate the effectiveness of the proposed methods, a series of comparative trials are performed on a variety of synthetic data sets. More specifically, the new methods are used to deal with problems associated with background subtraction (foreground object detection), and removing shadows and peculiarities from images of faces. Our experimental results illustrate that our new frameworks are more effective and accurate when compared with other methods.

A novel face recognition method: Using random weight networks and quasi-singular value decomposition

Abstract This paper designs a novel human face recognition method, which is mainly based on a new feature extraction method and an efficient classifier – random weight network (RWN). Its innovation of the feature extraction is embodied in the good fusion of the geometric features and algebraic features of the original image. Here the geometric features are acquired by means of fast discrete curvelet transform (FDCT) and 2-dimensional principal component analysis (2DPCA), while the algebraic features are extracted by a proposed quasi-singular value decomposition (Q-SVD) method that can embody the relations of each image under a unified framework. Subsequently, the efficient RWN is applied to classify these fused features to further improve the recognition rate and the recognition speed. Some comparison experiments are carried out on six famous face databases between our proposed method and some other state-of-the-art methods. The experimental results show that the proposed method has an outstanding superiority in the aspects of separability, recognition rate and training time.

Human face recognition based on ensemble of polyharmonic extreme learning machine

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.

A novel approach for fault diagnosis of induction motor with invariant character vectors

This paper proposes a novel approach for the fault diagnosis of induction motors. The invariant character vectors of fault signals are first extracted from the training samples. A single-class support vector machine (SC-SVM) is then used to detect the occurrence of faults, and the obtained invariant character vectors are employed as the desired references to classify the faults associated with the nearest neighbor classifier. The new diagnosis algorithm is validated for an induction motor (Y132S-4), which has shown excellent performance.

Image super-resolution via adaptive sparse representation

Existing methods for image super-resolution (SR) usually use 1-regularization and 2-regularization to emphasize the sparsity and the correlation, respectively. In order to coordinate the sparsity and correlation synthetically, this paper proposes an adaptive sparse coding based super-resolution method, named ASCSR method, by means of establishing a regularization model, which effectively integrates sparsity and correlation as a regularization term in the model, and adaptively harmonizes the sparse representation and the collaborative representation. The method can balance the relation between the sparsity and collaboration adaptively via producing a suitable coefficient. To approximate the optimal solution of the model, we adopt a current popular and effective method, i.e., the alternating direction method of multipliers (ADMM). Compared with some other existing SR methods, the experimental results demonstrate that the proposed ASCSR method possesses outstanding performance in term of reconstruction effect, stability to the dictionary, and the noise immunity.

Segmentation of White Blood Cells Image Using Adaptive Location and Iteration

Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the GrabCut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.

Leukocyte image segmentation using feed forward neural networks with random weights

As we know, segmentation is an important countermeasure in the study of automated leukocyte image recognition. This paper proposes a novel method for leukocyte image segmentation, which is based on converting the segmentation to a classification issue. First, an effective classifier called feed forward neural network with random weights is employed to classify all the pixels in a leukocyte image. Then, according to the classification results, the regions of nucleus and cytoplasm are extracted, respectively, to achieve the segmentation. The experiments show that the proposed method is more effective compared with some existing approaches, and can segment the nucleus and cytoplasm well. Meanwhile, the advantage of the proposed method in leukocyte recognition is also reviewed and analyzed.

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods.

A novel segmentation algorithm for nucleus in white blood cells based on low-rank representation

White blood cells (WBCs) segmentation is a challenging problem in the study of automated morphological systems, due to both the complex nature of the cells and the uncertainty that is present in video microscopy. This paper investigates how to boost the effects of region-based nucleus segmentation in WBCs by means of optimal thresholding and low-rank representation. The main idea is firstly using optimal thresholding to obtain the possible uniform WBC regions in the input image. After that, a manifold-based low-rank representation technique is employed to infer a unified affinity matrix that implicitly encodes the segmentation of the pixels of possible WBC regions. This is achieved by separating the low-rank affinities from the feature matrix into a pair of sparse and low-rank matrices. The experiments show that the proposed method is possible to produce better segmentation results compared with existing approaches.