Weighted sparse representation for face recognition

Locality-Constrained Discriminative Matrix Regression for Robust Face Identification

Regression-based methods have been widely applied in face identification, which attempts to approximately represent a query sample as a linear combination of all training samples. Recently, a matrix regression model based on nuclear norm has been proposed and shown strong robustness to structural noises. However, it may ignore two important issues: the label information and local relationship of data. In this article, a novel robust representation method called locality-constrained discriminative matrix regression (LDMR) is proposed, which takes label information and locality structure into account. Instead of focusing on the representation coefficients, LDMR directly imposes constraints on representation components by fully considering the label information, which has a closer connection to identification process. The locality structure characterized by subspace distances is used to learn class weights, and the correct class is forced to make more contribution to representation. Furthermore, the class weights are also incorporated into a competitive constraint on the representation components, which reduces the pairwise correlations between different classes and enhances the competitive relationships among all classes. An iterative optimization algorithm is presented to solve LDMR. Experiments on several benchmark data sets demonstrate that LDMR outperforms some state-of-the-art regression-based methods.

Research on the effect of monitoring the individual learning status of students in English teaching on the improvement of teaching effect

In order to improve the effect of English teaching, this paper proposes a complex interactive collective behavior recognition method based on dynamic kernel density, which can more accurately identify classroom behavior patterns with complex interactions. Moreover, this paper proposes a dynamic crowd path planning method based on the emotional communication model. This method introduces the subjective factor of emotions in path planning, which can more realistically show the diversity of group path choices. At the same time, this paper combines the actual needs of English teaching to build a student status monitoring system based on group feature recognition, which can monitor the status of groups and individuals in real time. In addition, this paper introduces emotion algorithms to recognize student emotions, and combines action recognition to complete student state monitoring. Finally, this paper designs experiments to verify the performance of the system constructed in this paper. The research results show that the system constructed in this paper has certain effects and can provide theoretical reference for related research.

Fuzzy processing system for psychological pressure of English teachers at work based on analysis of online teaching video

Due to the fast pace of modern online English teaching and the complicated teaching relationship, English teachers face increasing pressure in teaching work, which easily leads to various psychological problems. In view of this, based on theoretical results and expert experience in the field of psychological pressure of English teachers, this paper uses fuzzy processing systems and fuzzy weighted logic inference theory to establish a knowledge base in the field of psychological health and an evaluation model for psychological pressure of English teachers. Moreover, this paper carries out knowledge representation and reasoning on the knowledge of psychological health theory, and applies the evaluation reasoning model to the expert system of psychological health evaluation of English teachers to finally design and realize the expert system of psychological health evaluation of English teachers based on fuzzy weighted logic. Finally, this paper designs experiments to verify the system performance. The research results show that the system’s data processing speed and the accuracy of the evaluation results of psychological stress of English teachers meet the actual needs.

Real-time monitoring of physical education classroom in colleges and universities based on open IoT and cloud computing

The teaching effect of college physical education classroom needs to be combined with artificial intelligence system. From the actual situation, the current college physical education classroom is mostly based on manual teaching and manual management, so the teaching effect is not good. In order to change the traditional teaching mode and improve the classroom detection effect, based on the open Internet of Things and cloud computing technology, this paper builds a real-time monitoring system of college physical education classroom, and proposes a number of new and improved algorithms, which provide a theoretical and technical basis for the application of automatic identity positioning in large scenes. Moreover, this study obtains field scenes through field image data collection and field data processing, and then combines the regional scenes with field measured data to verify accuracy and trends to obtain students’ morphological characteristics. In addition, this paper designs practical experiments to verify the system performance. The research results show that the intelligent system constructed in this paper has certain effects and can be applied to physical education.

Autonomous learning of foreign language based on facial emotion recognition and cloud computing

Due to the limitations of the learning environment and unguided guidance, students’ autonomous learning of foreign languages after class is not effective. In order to improve the efficiency of autonomous learning of foreign languages, this paper builds a foreign language self-learning system based on facial emotion recognition algorithm and cloud computing platform. Moreover, this paper uses emotion recognition algorithms to identify students’ status and guide them to improve students’ autonomous learning and improve the system’s operating efficiency through cloud computing platforms. In addition, this article combines the needs of autonomous learning to perform facial emotion matching and builds the corresponding functional modules of the system according to the requirements of autonomous learning and designs a sophisticated three-level network structure to achieve a balance between detection performance and real-time performance. In order to verify the performance of the system, an experiment was carried out through the accuracy rate of student’s autonomous state emotion recognition and the English improvement of students’ autonomous learning. The research results show that the foreign language autonomous learning system constructed in this paper has good performance.

Cross-cultural O2O English teaching based on AI emotion recognition and neural network algorithm

Cross-cultural English teaching is limited by the influence of traditional teaching models, resulting in poor teaching results. In order to improve the efficiency of cross-cultural English teaching, with the support of AI emotion recognition and neural network algorithm, this paper builds a cross-cultural O2O English teaching system with intelligent recognition and management. Moreover, this research uses background models to detect and track targets, to realize the full recognition of students’ emotions, and to facilitate teachers to effectively control online teaching. In addition, combined with online and offline teaching, this study uses neural network algorithm to stabilize the system and perform data processing, construct an overall O2O English teaching model according to actual needs, and formulate the corresponding teaching process. In order to verify the performance of the model, this study starts from two aspects: system performance test and system practice effect and uses statistical methods to collect and process data. The test results show that the model constructed in this paper has good performance and meets expectations.

Intelligent analysis of classroom student state based on neural network algorithm and emotional feature recognition

Classroom teaching in the context of artificial intelligence needs to be combined with modern intelligent recognition technology to improve classroom teaching efficiency. In order to study the auxiliary teaching system for classroom student management, this article is based on neural network technology and emotional feature recognition algorithm, and according to the actual situation of classroom teaching, an intelligent analysis system for classroom student status is constructed. The system simulates the RFID mode to tag the students. Moreover, this article sets the system function module according to the actual teaching management needs and designs the learning algorithm of the quantitative assessment model. In addition, this study uses machine learning methods to design the quantitative evaluation index system, logistic regression scoring algorithm and model training algorithm. Finally, this study uses the neural network algorithm as the comparison algorithm to verify the performance of the constructed model and analyzes the comparison results through chart comparison. The research results show that the model proposed in this paper has good performance and can be applied to practical classrooms.

Mental health diagnosis of college students based on facial recognition and neural network

In recent years, college campus incidents caused by mental health problems have been increasing year by year, and college students’ mental health problems have become the focus of attention of schools, society and parents. Based on this, this paper proposes a facial emotion recognition method for college students. By using moving target detection, target classification, target tracking, and a series of image preprocessing techniques, this method achieves intelligent monitoring of the area where college students are located and can automatically alert when a potentially dangerous target is found. Moreover, this method uses a combination of shape features and motion features to select and extract feature quantities. In addition, the method calculates the similarity between the target and candidate target corresponding sub-models, and according to the ability of each feature to distinguish between the target and the background, monitors the student’s mental health in real time and prevents various problems from occurring. Through experimental research, we can see that the model constructed in this paper has good performance.

Intelligent English classroom video clarity improvement based on motion compensation and grid flow

For the English classroom teaching video denoising algorithm, it is not only necessary to consider whether the noise removal of the output video is thorough, but also to consider the actual operating efficiency and robustness of the algorithm. In the process of the thesis research, after reading a large number of internal and external documents on video denoising algorithms and analyzing the pros and cons of various denoising algorithms, this paper proposes a new video denoising algorithm, which uses the recently proposed grid flow motion model based on camera motion compensation to generate denoised video. Compared with the current advanced video denoising schemes, our method processes noisy frames faster and has good robustness. In addition, this article improves the algorithm framework so that the algorithm can not only deal with offline video denoising, but also deal with online video denoising.

Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation

In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.

A hybrid eye movement feature recognition of classroom students based on machine learning

Online classroom teaching is difficult to identify students’ learning status in real time. Therefore, we need to combine intelligent image recognition technology to analyze student status through eye movement features. This study solves the problem of inaccurate positioning of the initial position of the shape model in the process of eyelid matching through machine learning. Moreover, this study improves the algorithm and uses the AK-EYE model based on the combination of ASM algorithm and Kalman filtering to establish a local feature model for each feature point. According to the gray information in the normal direction of the feature point, the local gray information is modeled. After training through the sample set to obtain the state model, the target eye can be searched, and the pose parameters can be determined. Finally, this study designs a control experiment to analyze the performance of the model proposed in this study. The research shows that the algorithm proposed in this paper has a high recognition accuracy and has a practical basis, which can be used as one of the subsequent classroom teaching system algorithms.

Performance analysis of distance teaching classroom based on machine learning and virtual reality

In remote intelligent teaching, the facial expression features can be recorded in time through facial recognition, which is convenient for teachers to judge the learning status of students in time and helps teachers to change teaching strategies in a timely manner. Based on this, this study applies machine learning and virtual reality technology to distance classroom teaching. Moreover, this study uses different channels to automatically learn global and local features related to facial expression recognition tasks. In addition, this study integrates the soft attention mechanism into the proposed model so that the model automatically learns the feature maps that are more important for facial expression recognition and the salient regions within the feature maps. At the same time, this study performs weighted fusion on the features extracted from different branches, and uses the fused features to re-recognize student features. Finally, this study analyzes the results of this paper through control experiments. The research results show that the algorithm proposed in this paper has good performance and can be applied to the distance teaching system.

Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification

The sparse representation-based classification (SRC) has been utilized in many applications and is an effective algorithm in machine learning. However, the performance of SRC highly depends on the data distribution. Some existing works proved that SRC could not obtain satisfactory results on uncontrolled data sets. Except the uncontrolled data sets, SRC cannot deal with imbalanced classification either. In this paper, we proposed a model named sparse supervised representation classifier (SSRC) to solve the above-mentioned issues. The SSRC involves the class label information during the test sample representation phase to deal with the uncontrolled data sets. In SSRC, each class has the opportunity to linearly represent the test sample in its subspace, which can decrease the influences of the uncontrolled data distribution. In order to classify imbalanced data sets, a class weight learning model is proposed and added to SSRC. Each class weight is learned from its corresponding training samples. The experimental results based on the AR face database (uncontrolled) and 15 KEEL data sets (imbalanced) with an imbalanced rate ranging from 1.48 to 61.18 prove SSRC can effectively classify uncontrolled and imbalanced data sets.

FCTP-WSRC: Protein-Protein Interactions Prediction via Weighted Sparse Representation Based Classification

The task of predicting protein–protein interactions (PPIs) has been essential in the context of understanding biological processes. This paper proposes a novel computational model namely FCTP-WSRC to predict PPIs effectively. Initially, combinations of the F-vector, composition (C) and transition (T) are used to map each protein sequence onto numeric feature vectors. Afterwards, an effective feature extraction method PCA (principal component analysis) is employed to reconstruct the most discriminative feature subspaces, which is subsequently used as input in weighted sparse representation based classification (WSRC) for prediction. The FCTP-WSRC model achieves accuracies of 96.67%, 99.82%, and 98.09% for H. pylori, Human and Yeast datasets respectively. Furthermore, the FCTP-WSRC model performs well when predicting three significant PPIs networks: the single-core network (CD9), the multiple-core network (Ras-Raf-Mek-Erk-Elk-Srf pathway), and the cross-connection network (Wnt-related Network). Consequently, the promising results show that the proposed method can be a powerful tool for PPIs prediction with excellent performance and less time.

Weighted sparse representation for classification of motor imagery EEG signals

Motor imagery (MI) based brain-computer interface systems (BCIs) are highly in demand for many real-time applications such as hands and touch-free text entry, prosthetic arms, virtual reality, movement of wheelchairs, etc. Traditional sparse representation based classification (SRC) is a thriving technique in recent years and has been a successful approach for classifying MI EEG signals. To further improve the capability of SRC, in this paper, a weighted SRC (WSRC) has been proposed for classifying two-class MI tasks (right-hand, right-foot). WSRC constructs a weighted dictionary according to the dissimilarity information between the test data and the training samples. Then for the given test data the sparse coefficients are computed over the weighted dictionary using l₀-minimization problem. The sparse solution obtained using WSRC gives better discriminative information than SRC and as a consequence, WSRC proves to be superior for MI EEG classification. The experimental results substantiate that WSRC is more efficient and accurate than SRC.

Collaborative Representation Using Non-Negative Samples for Image Classification

Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using l2 regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by l2 minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC.

A Robust Group-Sparse Representation Variational Method with applications to Face Recognition

In this paper we propose a Group-Sparse Representation based method with applications to Face Recognition (GSR-FR). The novel sparse representation variational model includes a non-convex sparsity-inducing penalty and a robust non-convex loss function. The penalty encourages group sparsity by using approximation of the ℓ0-quasinorm, and the loss function is chosen to make the algorithm robust to noise, occlusions and disguises. The solution of the non-trivial non-convex optimization problem is efficiently obtained by a majorization-minimization strategy combined with forward-backward splitting, which in particular reduces the solution to a sequence of easier convex optimization sub-problems. Extensive experiments on widely used face databases show the potentiality of the proposed model and demonstrate that the GSR-FR algorithm is competitive with state-of-the-art methods based on sparse representation, especially for very low dimensional feature spaces.

Discriminant WSRC for Large-Scale Plant Species Recognition

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Cost Sensitive Face Security Verification Based on Limited Expression-Pose Pattern Sparse Representation Model

Face security verification has been recognized as a cost sensitive classification problem. To deal with this problem, many cost sensitive classifiers have been proposed to alleviate the facial variation. However, no suficient attention is paid to the research on sparse representation cost sensitive face verification. In this paper, we proposed a coarse to find face security verification method, called cost sensitive face verification based on limited expression-pose pattern (CSFV_LEP) for security verification task. The main contributions of the proposed method are as follows: (1) a discrimination dictionary is established in a common way to discriminate whether visitor is an internal member; (2) a confirmation dictionary, which contains only limited expression-pose details, is used to confirm whether this is the correct classification. Meanwhile, we use adaptive weight matrix from similarity information to enhance the robustness of the two dictionaries. Experiments show that, the proposed method has high verifiable and ideal secure performance according to accuracy and efficiency, and contribute to reduce cost penalization during the sparse coding stages.

Jaccard distance based weighted sparse representation for coarse-to-fine plant species recognition

Leaf based plant species recognition plays an important role in ecological protection, however its application to large and modern leaf databases has been a long-standing obstacle due to the computational cost and feasibility. Recognizing such limitations, we propose a Jaccard distance based sparse representation (JDSR) method which adopts a two-stage, coarse to fine strategy for plant species recognition. In the first stage, we use the Jaccard distance between the test sample and each training sample to coarsely determine the candidate classes of the test sample. The second stage includes a Jaccard distance based weighted sparse representation based classification(WSRC), which aims to approximately represent the test sample in the training space, and classify it by the approximation residuals. Since the training model of our JDSR method involves much fewer but more informative representatives, this method is expected to overcome the limitation of high computational and memory costs in traditional sparse representation based classification. Comparative experimental results on a public leaf image database demonstrate that the proposed method outperforms other existing feature extraction and SRC based plant recognition methods in terms of both accuracy and computational speed.

Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning

In this paper, we consider the robust face recognition problem via iterative re-constrained group sparse classifier (IRGSC) with adaptive weights learning. Specifically, we propose a group sparse representation classification (GSRC) approach in which weighted features and groups are collaboratively adopted to encode more structure information and discriminative information than other regression based methods. In addition, we derive an efficient algorithm to optimize the proposed objective function, and theoretically prove the convergence. There are several appealing aspects associated with IRGSC. First, adaptively learned weights can be seamlessly incorporated into the GSRC framework. This integrates the locality structure of the data and validity information of the features into l_2,p -norm regularization to form a unified formulation. Second, IRGSC is very flexible to different size of training set as well as feature dimension thanks to the l_2,p -norm regularization. Third, the derived solution is proved to be a stationary point (globally optimal if p ≥ 1 ). Comprehensive experiments on representative data sets demonstrate that IRGSC is a robust discriminative classifier which significantly improves the performance and efficiency compared with the state-of-the-art methods in dealing with face occlusion, corruption, and illumination changes, and so on.

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.