In this paper, a novel group sparse canonical correlation analysis (GSCCA) method is proposed for simultaneous electroencephalogram (EEG) channel selection and emotion recognition. GSCCA is a group ...sparse extension of the conventional CCA method to model the linear correlationship between emotional EEG class label vectors and the corresponding EEG feature vectors. In contrast to conventional CCA method or previous GSCCA methods, a major advantage of our GSCCA method is the ability of handling the group feature selection problem from raw EEG features, which makes it very suitable for simultaneously coping with both EEG emotion recognition and automatic channel selection issues where each EEG channel is associated with a group of raw EEG features. To deal with EEG emotion recognition problem, we adopt the popularly used frequency feature to describe the EEG signal by dividing the full EEG frequency band into five parts, i.e., δ, θ, α, β, and γ frequency bands, and then extract the frequency band features from each band for GSCCA model learning and emotion recognition. Finally, we conduct extensive experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG-based emotion recognition approaches.
In this paper, a novel multi-view facial expression recognition method is presented. Different from most of the facial expression methods that use one view of facial feature vectors in the expression ...recognition, we synthesize multi-view facial feature vectors and combine them to this goal. In the facial feature extraction, we use the grids with multi-scale sizes to partition each facial image into a set of sub regions and carry out the feature extraction in each sub region. To deal with the prediction of expressions, we propose a novel group sparse reduced-rank regression (GSRRR) model to describe the relationship between the multi-view facial feature vectors and the corresponding expression class label vectors. The group sparsity of GSRRR enables us to automatically select the optimal sub regions of a face that contribute most to the expression recognition. To solve the optimization problem of GSRRR, we propose an efficient algorithm using inexact augmented Lagrangian multiplier (ALM) approach. Finally, we conduct extensive experiments on both BU-3DFE and Multi-PIE facial expression databases to evaluate the recognition performance of the proposed method. The experimental results confirm better recognition performance of the proposed method compared with the state of the art methods.
In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition ...method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.
Over the centuries, infectious diseases caused by viruses have seriously threatened human health globally. Viruses are responsible not only for acute infections but also many chronic infectious ...diseases. To prevent diseases caused by viruses, the discovery of effective antiviral drugs, in addition to vaccine development, is important. Green tea catechins (GTCs) are polyphenolic compounds from the leaves of
. In recent decades, GTCs have been reported to provide various health benefits against numerous diseases. Studies have shown that GTCs, especially epigallocatechin-3-gallate (EGCG), have antiviral effects against diverse viruses. The aim of this review is to summarize the developments regarding the antiviral activities of GTCs, to discuss the mechanisms underlying these effects and to offer suggestions for future research directions and perspectives on the antiviral effects of EGCG.
Micro-expression recognition aims to infer genuine emotions that people try to conceal from facial video clips. It is a very challenging task because micro-expressions have a very low intensity and ...short duration, which makes micro-expressions difficult to observe. Recently, researchers have designed various spatiotemporal descriptors to describe micro-expressions. It is notable that for better capturing the low-intensity facial muscle movement, a fixed spatial division grid, <inline-formula><tex-math notation="LaTeX">8\times 8</tex-math></inline-formula> for example, is commonly used to partition the facial images into a few facial blocks before extracting descriptors. However, it is hard to choose an ideal division grid for different micro-expression samples because the division grids affect the discriminative ability of spatiotemporal descriptors to distinguish micro-expressions. To address this problem, in this paper, we design a hierarchical spatial division scheme for spatiotemporal descriptor extraction. By using the proposed scheme, it would not be a problem to determine which division grid is most suitable regarding different micro-expression samples. Furthermore, we propose a kernelized group sparse learning (KGSL) model to process hierarchical scheme based spatiotemporal descriptors such that they are more effective for micro-expression recognition tasks. To evaluate the performance of the proposed micro-expression recognition method consisting of the hierarchical scheme based spatiotemporal descriptors and KGSL, extensive experiments are conducted on two public micro-expression databases: CASME II and SMIC. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.
Cross-corpus speech emotion recognition has recently received considerable attention due to the widespread existence of various emotional speech. It takes one corpus as the training data aiming to ...recognize emotions of another corpus, and generally involves two basic problems, i.e., feature matching and feature selection. Many previous works study these two problems independently, or just focus on solving the first problem. In this paper, we propose a novel algorithm, called feature selection based transfer subspace learning (FSTSL), to address these two problems. To deal with the first problem, a latent common subspace is learnt by reducing the difference of different corpora and preserving the important properties. Meanwhile, we adopt the <inline-formula><tex-math notation="LaTeX">l_{2,1}</tex-math> <mml:math><mml:msub><mml:mi>l</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="song-ieq1-2800046.gif"/> </inline-formula>-norm on the projection matrix to deal with the second problem. Besides, to guarantee the subspace to be robust and discriminative, the geometric information of data is exploited simultaneously in the proposed FSTSL framework. Empirical experiments on cross-corpus speech emotion recognition tasks demonstrate that our proposed method can achieve encouraging results in comparison with state-of-the-art algorithms.
In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature ...transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data and sent to a well-designed DNN model for learning optimal discriminative features for expression classification. The proposed DNN model employs several layers to characterize the corresponding relationship between the SIFT feature vectors and their corresponding high-level semantic information. By training the DNN model, we are able to learn a set of optimal features that are well suitable for classifying the facial expressions across different facial views. To evaluate the effectiveness of the proposed method, two nonfrontal facial expression databases, namely BU-3DFE and Multi-PIE, are respectively used to testify our method and the experimental results show that our algorithm outperforms the state-of-the-art methods.
In this letter, a novel cross-corpus speech emotion recognition (SER) method using domain-adaptive least-squares regression (DaLSR) model is proposed. In this method, an additional unlabeled data set ...from target speech corpus is used to serve as an auxiliary data set and combined with the labeled training data set from source speech corpus for jointly training the DaLSR model. In contrast to the traditional least-squares regression (LSR) method, the major novelty of DaLSR is that it is able to handle the mismatch problem between source and target speech corpora. Hence, the proposed DaLSR method is very suitable for coping with cross-corpus SER problem. For evaluating the performance of the proposed method in dealing with the cross-corpus SER problem, we conduct extensive experiments on three emotional speech corpora and compare the results with several state-of-the-art transfer learning methods that are widely used for cross-corpus SER problem. The experimental results show that the proposed method achieves better recognition accuracies than the state-of-the-art methods.
Fisher linear discriminant analysis (LDA) is a classical subspace learning technique of extracting discriminative features for pattern recognition problems. The formulation of the Fisher criterion is ...based on the L2-norm, which makes LDA prone to being affected by the presence of outliers. In this paper, we propose a new method, termed LDA-L1, by maximizing the ratio of the between-class dispersion to the within-class dispersion using the L1-norm rather than the L2-norm. LDA-L1 is robust to outliers, and is solved by an iterative algorithm proposed. The algorithm is easy to be implemented and is theoretically shown to arrive at a locally maximal point. LDA-L1 does not suffer from the problems of small sample size and rank limit as existed in the conventional LDA. Experiment results of image recognition confirm the effectiveness of the proposed method.
Recently, micro-expression recognition has attracted lots of researchers' attention due to its potential value in many practical applications, e.g., lie detection. In this paper, we investigate an ...interesting and challenging problem in micro-expression recognition, i.e., cross-database micro-expression recognition, in which the training and testing samples come from different micro-expression databases. Under this problem setting, the consistent feature distribution between the training and testing samples originally existing in conventional micro-expression recognition would be seriously broken, and hence, the performance of most current well-performing micro-expression recognition methods may sharply drop. In order to overcome it, we propose a simple yet effective framework called domain regeneration (DR) in this paper. The DR framework aims at learning a domain regenerator to regenerate the micro-expression samples from source and target databases, respectively, such that they can abide by the same or similar feature distributions. Thus, we are able to use the classifier learned based on the labeled source micro-expression samples to predict the label information of the unlabeled target micro-expression samples. To evaluate the proposed DR framework, we conduct extensive cross-database micro-expression recognition experiments designed based on the Spontaneous Micro-Expression Database and Chinese Academy of Sciences Micro-Expression II Database. Experimental results show that compared with the recent state-of-the-art cross-database emotion recognition methods, the proposed DR framework has more promising performance.
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