Semi-supervised discriminant analysis method via weighted low-rank representation and adaptive neighbor selection

An empirical study of the impact of masks on face recognition

Face recognition with single training sample has the problem that the sample is single and face pattern is changeable. The rate of traditional face recognition methods declines seriously in the case of single training sample. Thus, studying the face recognition method which is appropriate for single training sample is challenging in face recognition field. In this paper, in order to solve the problem of single training sample, sample extension method is adopted to expand fourteen virtual samples. And for the problem of changeable face pattern, like pose, expressions, illumination etc., this paper uses image blocking and applies weighted two-directional 2DPCA to extract the local feature information of face image. Further, fuse the local feature information of sub-image to face recognition. The experimental results on ORL and YALE face database show that the method in this paper achieves good recognition effect.

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear. The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable. Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation. Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data. Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods.

The paper made trend analysis on global patent in the field of face recognition by taking advantage of retrieval and analysis tool of Innography, conducted specific comparison on high-intensive patents of three countries, China, America and Korea which were top three in number of global patent applications, and analyzed existent advantages and gap in patent application and layout in China. Although our country started later in the field of face recognition, it had been prosperous development trend on number of patent applications due to technology innovation and policy support. In terms of high-intensive patent, there was a certain gap with the United States. Accordingly, This paper proposed suggestions for face recognition technology in China: To improve quality of patent application; to strengthen cooperation among enterprises, universities and other scientific research institutions in order to realize a win-win situation; To fully cultivate the talents of face recognition and artificial intelligence.

The application scenarios of face recognition are very wide, and it has become a research hotspot in the field of computer vision. The deep learning technology represented by the convolutional neural network has shown great advantages in the field of face recognition. Its network structure is developing in a deeper and wider direction, and has achieved better and better recognition results. Due to its complex structure, the training is difficult to converge and occupies a large amount of memory and takes a long time to load, which is not conducive to the application in practical engineering. In order to solve this problem, this paper studies the application of deep learning in the field of face recognition, and proposes an improved residual neural network. On the basis of the original model, this model sacrifices less accuracy, and improves the speed of face recognition to a greater extent. This makes the model of great significance in the field of engineering practice.

Introduction to face recognition and evaluation of algorithm performance

Multiple kernel locality-constrained collaborative representation-based discriminant projection for face recognition

Oscillator noise is generally modeled by a power law spectral density. Thus it is possible to characterize different noise sources, each of them corresponding to a particular power law. The measurement of the contribution of these sources is necessary to know their origin and to remedy these causes in order to improve oscillator performance. Usually, an estimation of the different types of noise present in a signal is obtained by using a variance. However, the sensitivity of these variances differs for each type of noise and then limits this method. On the other hand, the use of several variances, each of them more sensitive to one type of noise, permits one to notably improve the measurement accuracy. The method suggested here uses as many different variances as there are types of noise to measure. The improvement of measurement accuracy of the noise coefficient is discussed in this paper.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this work, the effects of background noises on nonlinear dynamics of a modelled standing-wave thermoacoustic system with subcritical Hopf bifurcation behaviors are studied. These noises include (1) pressure-coupled (acoustic), (2) velocity-coupled (flow), and (3) external combustion noise. It is found that these three types of background noises play important, but different roles in changing the hysteresis width and stability boundary. In addition, the stochastic transition from stability to instability is investigated, as the noise intensity is varied. Two different stochastic P-bifurcations are identified. One is related to a craterlike probability density distribution. The other is associated with a probability density distribution characterized with two peaks and one trough. With each type of noise affecting the system's stochastic behaviors being evaluated, the effect of two different types of noises is then studied. It is shown that the combined noises (types 1 and 2) cannot only destabilize global stable thermoacoustic system, but also stabilize linearly unstable system. This depends strongly on the superimposition form of the two types of noises. In addition, when the thermoacoustic system is disturbed by the combined noise (types 3 and 1 or types 3 and 2), the transition process is dominated by the combustion noise.

Traditional clustering methods are not very effective when dealing with high-dimensional and huge datasets. Even if there are some traditional dimensionality reduction methods such as Principal components analysis (PCA), Linear discriminant analysis (LDA) and T-distributed stochastic neighbor embedding (T-SNE), they still can not significantly improve the effect of the clustering algorithm in this scenario. Recent studies have combined Non-linear dimensionality reduction achieved by deep neural networks with hard-partition clustering, and have achieved reliability results, but these methods can not update the parameters of dimensionality reduction and clustering at the same time. We found that soft-partition clustering can be well combined with deep embedding, and the membership of Fuzzy c-means (FCM) can solve the problem that gradient descent can not be implemented because the assignment process of the hard-partition clustering algorithm is discrete, so that the algorithm can update the parameters of deep neural network (DNN) and cluster centroids at the same time. We build an continuous objective function that combine the soft-partition clustering with deep embedding, so that the learning representations can be cluster-friendly. The experimental results show that our proposed method of simultaneously optimizing the parameters of deep dimensionality reduction and clustering is better than the method with separate optimization.

Big data refers to huge information whose quantity is beyond the capacity of the system to manage, process and capture. On the basis of the biological and behavioral characteristics of the human, information gathered from which person can be recognized and referred as biometrics. Example includes finger print, face, voice and behavioral analysis etc. Among these, face recognition will not make use of any physical contact with the biometrics system; it is more secure and effective. Hence face recognition becomes one of the best technologies in the biometrics field. Although plenty of efforts have been carried out in the field of face recognition, this work has mark with many challenges in general settings. The criteria behind the successful face recognition systems are developed only under constrained situations with small sized data bases. Hence there is necessary to work under general setting that fit to big data for face recognition. This paper, aims to increase performance and accuracy of face recognition by considering face spoofing attack (veracity). Result analysis of our proposed work ensures increase in accuracy compared to other methods.

A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image

Face recognition is the process of identifying and verifying a person from image or video. A significant amount of contributions have already been made by the researchers in this field of face identity techniques and recognition. In this paper, we further explored and investigated the evolution of face recognition methods from low-level features extracted from global features such as PCA, eigen face and SVM-based methods to high-level features extracted from deep learning models such as DeepFace and VGGFace. We also discussed the challenges such as illumination and pose variation and available standard data sets such as LFW and Yale data set in the field of face recognition.KeywordsFace recognitionGlobal methodsDeep learning methodsPCASVMDeepFaceVGGFace