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Error between an occluded image and a “clean” one. We reshape the pixels in the two samples (a) into vectors and calculate the difference (b). Obviously, the distribution of error is like a comb, which indicates that the error exists in a few pixels and the others are “clean.”
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A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm - avoiding to process useless information in the training dictionary. For the test samples...
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Since sparse representation (SR) was first introduced into robust face recognition, the argument has lasted for several years about whether sparsity can improve robust face recognition or not. Some work argued that the robust sparse representation (RSR) model has a similar recognition rate as non-sparse solution, while it needs a much higher comput...
Dictionary learning has recently attracted a great deal of attention due to its efficacy in sparse representation based image classification task. There are two main limitations of the Sparse Representation based Classification (SRC) for applications. One is that the training data is required to be less corrupted, and the other is that each class s...
Error detection is an important approach to improve the robustness of face recognition method. However, it is hard to directly detect the invalid pixels in a facial image. The authors decompose the hard problem into many simpler sub-problems in this study. That is, the error detection process of pixels is divided into multiple phases and a portion...
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... The basic idea of facial emotion based on sparse representation [8,23] is that the training sample is composed of a sample set, and the test sample can be represented by the sparse representation. Using signal sparse representation not only could greatly reduce the computational complexity of the algorithm, but also could improve the accuracy of classification. ...
... where α i,j ∈ R, the k class, n training samples are combined to form the entire training set matrix A , A ∈ R m * n , test samples y could be expressed as y = Ax, x may be referred to as a coefficient vector of the linear expansion of the signal y in the dictionary A. For the x can take a variety of ways to solve, this paper uses orthogonal matching pursuit (OMP) algorithm to solve the problem. The orthogonal match pursuit(OMP) algorithm [23,24] updates the index set by finding the atoms in the training sample matrix that are most linearly relevant to the residuals, and obtains the optimal projection of the test sample on the index set in each iteration, to ensure that the residuals are minimal, obtained sparse solution. The implementation process is as follows: ...
Due to the existence of false positive rate of the traditional depression diagnosis method, this paper proposes a multi-modal fusion algorithm based on speech signal and facial image sequence for depression diagnosis. Introduced spectrum subtraction to enhance depressed speech signal, and use cepstrum method to extract pitch frequency features with large variation rate and formant features with significant difference, the short time energy and Mel-frequency cepstral coefficients characteristic parameters for different emotion speeches are analyzed in both time domain and frequency domain, and establish a model for training and identification. Meanwhile, this paper implements the orthogonal match pursuit algorithm to obtain a sparse linear combination of face test samples, and cascade with voice and facial emotions based proportion. The experimental results show that the recognition rate based on the depression detection algorithm of fusion speech and facial emotions has reached 81.14%. Compared to the existing doctor’s accuracy rate of 47.3%, the accuracy can bring extra 71.54% improvement by combining with the proposed method of this paper. Additionally, it can easily apply to the hardware and software on the existing hospital instruments with low cost. Therefore, it is an accurate and effective method for diagnosing depression.
... Sparse representation (SR) theory [1,2] indicates that a signal can be represented by certain linear combination of a few atoms of a prespecified dictionary. It is an evolving field, with state-of-the-art results in many signal processing tasks, such as coding, denoising, face recognition, deblurring, and compressed sensing [3][4][5][6][7]. ...
... Note that the equivalent expression to (5) where the constraint is a fixed sparse representation error (SRE) level can also be formulated aŝ ...
Dictionary learning problem has become an active topic for decades. Most existing learning methods train the dictionary to adapt to a particular class of signals. But as the number of the dictionary atoms is increased to represent the signals much more sparsely, the coherence between the atoms becomes higher. According to the greedy and compressed sensing theories, this goes against the implementation of sparse coding. In this paper, a novel approach is proposed to learn the dictionary that minimizes the sparse representation error according to the training signals with the coherence taken into consideration. The coherence is constrained by making the Gram matrix of the desired dictionary approximate to an identity matrix of proper dimension. The method for handling the proposed model is mainly based on the alternating minimization procedure and, in each step, the closed-form solution is derived. A series of experiments on synthetic data and audio signals is executed to demonstrate the promising performance of the learnt incoherent dictionary and the superiority of the learning method to the existing ones.
... Sparse representation is one core issue of the modern theory of compressed sensing, and it is intended to represent a sample using a minimal number of nonzero coefficient terms. A number of works have shown that sparse representation and classification (SRC) algorithm can obtain a very high accuracy for image classification such as object recognition and image resolution [22][23][24][25]. However, the conventional SRC algorithm such as the one proposed in [22] has a relatively high computational cost, the improved sparse representation algorithm for fastener recognition is presented in this subsection, and our algorithm is mathematically tractable and computationally efficient. ...
... A number of works have shown that sparse representation and classification (SRC) algorithm can obtain a very high accuracy for image classification such as object recognition and image resolution [22][23][24][25]. However, the conventional SRC algorithm such as the one proposed in [22] has a relatively high computational cost, the improved sparse representation algorithm for fastener recognition is presented in this subsection, and our algorithm is mathematically tractable and computationally efficient. The first step in our algorithm aims at identifying and discarding the training samples that are "far" from the test sample and the second step then exploits the remaining training samples 1 , . . . ...
The detection of fastener defects is an important task for ensuring the safety of railway traffic. The earlier automatic inspection systems based on computer vision can detect effectively the completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we propose a method for detecting both partly worn and completely missing fasteners, the proposed algorithm exploits the first and second symmetry sample of original testing fastener image and integrates them for improved representation-based fastener recognition. This scheme is simple and computationally efficient. The underlying rationales of the scheme are as follows: First, the new virtual symmetrical images really reflect some possible appearance of the fastener; then the integration of two judgments of the symmetrical sample for fastener recognition can somewhat overcome the misclassification problem. Second, the improved sparse representation method discarding the training samples that are “far” from the test sample and uses a small number of samples that are “near” to the test sample to represent the test sample, so as to perform classification and it is able to reduce the side-effect of the error identification problem of the original fastener image. The experimental results show that the proposed method outperforms state-of-the-art fastener recognition methods.
Hydraulic components in mobile machines are subjected to varying conditions and loads, which limit their lifetime. In particular, high pressure drop rates are suspected to increase fatigue crack growth rates. Existing studies of the fluid‐structure interaction inside fatigue cracks during fast pressure drops do not explain increased crack growth in hydraulic systems. A laminar flow model was developed to simulate the fluid flow inside a crack under periodic pressure loads. The viscosity restrained the oil inside the crack, and the mechanical resistance of the closing crack led to a high pressure increase. Knowledge of the pressure profiles inside fatigue cracks helps estimate the effective fatigue damage during transient loads.
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition (FPPR) model and the dual-dictionary sparse representation classification (DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory (ORL) and face recognition technology (FERET) databases, and also reflect comparisons with SRC, linear regression classification (LRC), and two-phase test sample sparse representation (TPTSSR).
Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain's cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups.
