Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial ...Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial dynamics.Furthermore,existing methods often suffer from limited gen-eralization,as they primarily focus on single-dataset tasks with small sample sizes.To address these two issues,this paper proposes the cross-domain spatial-temporal graph convolutional network(GCN)(CDST-GCN)model,which comprises two primary components:a siamese attention spa-tial-temporal branch(SASTB)and a global-aware dynamic spatial-temporal branch(GDSTB).Specifically,SASTB utilizes a contrastive learning strategy to project macro-and micro-expressions into a shared,aligned feature space,actively addressing cross-domain discrepancies.Additionally,it integrates an attention-gated mechanism that generates adaptive adjacency matrices to flexibly model collaborative patterns among facial landmarks.While largely preserving the structural paradigm of SASTB,GDSTB enhances the feature representation by integrating global context extracted from a pretrained model.Through this dual-branch architecture,CDST-GCN success-fully models both the global and local spatial-temporal features.The experimental results on CASME II and SAMM datasets demonstrate that the proposed model achieves competitive perfor-mance.Especially in more challenging 5-class tasks,the accuracy of the model on CASME II dataset is as high as 80.5%.展开更多
Micro-expressions,fleeting involuntary facial cues lasting under half a second,reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy.Real-time recognition on resource-constrained embedded de...Micro-expressions,fleeting involuntary facial cues lasting under half a second,reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy.Real-time recognition on resource-constrained embedded devices remains challenging,as current methods struggle to balance performance and efficiency.This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy.Unlike prior simplistic feature fusion techniques,our novel multi-feature fusion strategy leverages temporal,spatial,and differential features to better capture dynamic changes.Enhanced by Residual Network(ResNet)architecture with channel and spatial attention mechanisms,the model improves feature representation while maintaining a lightweight design.Evaluations on SMIC,CASME II,SAMM,and their composite dataset show superior performance in Unweighted F1 Score(UF1)and Unweighted Average Recall(UAR),alongside faster detection speeds compared to existing algorithms.展开更多
Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most ...Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.展开更多
Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and tempo...Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction.Based on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is proposed.In the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped IBs.To alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,respectively.In the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be extracted.Finally,after fusing the global and local feature vectors,the ME time-series information is extracted by Bi-LSTM.The experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.展开更多
The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to ac...The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.展开更多
Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to ex...Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to extract spatial features of micro-expressions,and long short-term memory network(LSTM)to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG)using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.展开更多
The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dim...The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.展开更多
Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recogn...Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition(CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition. Methods In this paper, an adaptive spatio-temporal attention neural network(ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach,which extracts motion information among video frames that represent discriminative features of micro-expression.After preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment(CORAL) loss such that the source and target databases share similar distributions. Results To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks. Conclusions Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.展开更多
Micro-expression recognition has attracted growing research interests in the field of compute vision.However,micro-expression usually lasts a few seconds,thus it is difficult to detect.This paper presents a new framew...Micro-expression recognition has attracted growing research interests in the field of compute vision.However,micro-expression usually lasts a few seconds,thus it is difficult to detect.This paper presents a new framework to recognize micro-expression using pyramid histogram of Centralized Gabor Binary Pattern from Three Orthogonal Panels(CGBP-TOP)which is an extension of Local Gabor Binary Pattern from Three Orthogonal Panels feature.CGBP-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image sequences.In order to keep more local information of the face,CGBP-TOP is extracted based on pyramid subregions of the micro-expression video frame.The combination of CGBP-TOP and spatial pyramid can represent well and truly the facial movements of the micro-expression image sequences.However,the dimension of our pyramid CGBP-TOP tends to be very high,which may lead to high data redundancy problem.In addition,it is clear that people of different genders usually have different ways of micro-expression.Therefore,in this paper,in order to select the relevant features of micro-expression,the gender-specific sparse multi-task learning method with adaptive regularization term is adopted to learn a compact subset of pyramid CGBP-TOP feature for micro-expression classification of different sexes.Finally,extensive experiments on widely used CASME II and SMIC databases demonstrate that our method can efficiently extract micro-expression motion features in the micro-expression video clip.Moreover,our proposed approach achieves comparable results with the state-of-the-art methods.展开更多
Micro-expression is manifested through subtle and brief facial movements that relay the genuine person’s hidden emotion.In a sequence of videos,there is a frame that captures the maximum facial differences,which is c...Micro-expression is manifested through subtle and brief facial movements that relay the genuine person’s hidden emotion.In a sequence of videos,there is a frame that captures the maximum facial differences,which is called the apex frame.Therefore,apex frame spotting is a crucial sub-module in a micro-expression recognition system.However,this spotting task is very challenging due to the characteristics of micro-expression that occurs in a short duration with low-intensity muscle movements.Moreover,most of the existing automated works face difficulties in differentiating micro-expressions from other facial movements.Therefore,this paper presents a deep learning model with an attention mechanism to spot the micro-expression apex frame from optical flow images.The attention mechanism is embedded into the model so that more weights can be allocated to the regions that manifest the facial movements with higher intensity.The method proposed in this paper has been tested and verified on two spontaneous micro-expression databases,namely Spontaneous Micro-facial Movement(SAMM)and Chinese Academy of Sciences Micro-expression(CASME)II databases.The proposed system performance is evaluated by using the Mean Absolute Error(MAE)metric that measures the distance between the predicted apex frame and the ground truth label.The best MAE of 14.90 was obtained when a combination of five convolutional layers,local response normalization,and attention mechanism is used to model the apex frame spotting.Even with limited datasets,the results have proven that the attention mechanism has better emphasized the regions where the facial movements likely to occur and hence,improves the spotting performance.展开更多
As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in...As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in human-computer interaction,sentiment analysis,and security fields.However,the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods,raising concerns about serious privacy leakage and data sharing.To address these limitations,we investigate a federated learning scheme tailored specifically for this task.Our approach prioritizes user privacy by employing federated optimization techniques,enabling the aggregation of clients’knowledge in an encrypted space without compromising data privacy.By integrating established micro-expression recognition methods into our framework,we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms.To our knowledge,this marks the first application of federated learning to the micro-expression recognition task.展开更多
Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions ...Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions can be seen as the actors in cooperative group activities.In this paper,we propose a novel deep neural network model for objective class-based MER,which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks(GCN).Specifically,we propose two new strategies in our AU detection module for more effective AU feature learning:the attention mechanism and the balanced detection loss function.With these two strategies,features are learned for all the AUs in a unified model,eliminating the error-prune landmark detection process and tedious separate training for each AU.Moreover,our model incorporates a tailored objective class-based AU knowledge-graph,which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation.Extensive experiments on two tasks in MEGC 2018 show that our approach outperforms the current state-of-the-art methods in MER.Additionally,we also report our single model-based micro-expression AU detection results.展开更多
Micro-expression recognition is a substantive cross-study of psychology and computer science,and it has a wide range of applications(e.g.,psychological and clinical diagnosis,emotional analysis,criminal investigation,...Micro-expression recognition is a substantive cross-study of psychology and computer science,and it has a wide range of applications(e.g.,psychological and clinical diagnosis,emotional analysis,criminal investigation,etc.).However,the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features,which limits the improvement of micro-expression recognition accuracy.Therefore,we propose a multi-scale joint feature network based on optical flow images for micro-expression recognition.First,we generate an optical flow image that reflects subtle facial motion information.The optical flow image is then fed into the multi-scale joint network for feature extraction and classification.The proposed joint feature module(JFM)integrates features from different layers,which is beneficial for the capture of micro-expression features with different amplitudes.To improve the recognition ability of the model,we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone network.Our experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets(SMIC,CASME II,and SAMM)and a combined dataset(3 DB).展开更多
Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress,hide,disguise,or conceal.Such expressions can reflect a person&#...Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress,hide,disguise,or conceal.Such expressions can reflect a person's real emotions and have a wide range of application in public safety and clinical diagnosis.The analysis of facial micro-expressions in video sequences through computer vision is still relatively recent.In this research,a comprehensive review on the topic of spotting and recognition used in micro expression analysis databases and methods,is conducted,and advanced technologies in this area are summarized.In addition,we discuss challenges that remain unresolved alongside future work to be completed in the field of micro-expression analysis.展开更多
Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep perf...Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.展开更多
Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or fabricated.Micro-expression recognition(MER)is one of the most challenging topics in affective computing.It aims to ...Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or fabricated.Micro-expression recognition(MER)is one of the most challenging topics in affective computing.It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period.Recently,many deep learning-based MER methods have been developed.However,how to effectively capture subtle temporal variations for robust MER still perplexes us.We propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based MER.To explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t.original ME sequences and the counterfactuals w.r.t.counterfactually-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues.Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art methods.The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.展开更多
Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest....Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in comp...Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.展开更多
基金funded in part by the National Natural Science Foundation of China(Nos.62322111,62271289,62501186)the Natural Science Fund for Outstanding Young Scholars of Shandong Province(No.ZR2022YQ60)+4 种基金the Research Fund for the Taishan Scholar Project of Shandong Province(No.tsqn202306064)the Natural Science Fund for Distinguished Young Scientists of ShandongProvince(No.ZR2024JQ007)Shenzhen Science and Technology Program(No.JCYJ20240813101228036)Jinan“20 Terms of New Universities”Funding Project(No.202333035)the Fundamental Research funds for theCentral Universities(No.3072025CFJ0805).
文摘Although significant progress has been made in micro-expression recognition,effectively modeling the intricate spatial-temporal dynamics remains a persistent challenge owing to their brief duration and complex facial dynamics.Furthermore,existing methods often suffer from limited gen-eralization,as they primarily focus on single-dataset tasks with small sample sizes.To address these two issues,this paper proposes the cross-domain spatial-temporal graph convolutional network(GCN)(CDST-GCN)model,which comprises two primary components:a siamese attention spa-tial-temporal branch(SASTB)and a global-aware dynamic spatial-temporal branch(GDSTB).Specifically,SASTB utilizes a contrastive learning strategy to project macro-and micro-expressions into a shared,aligned feature space,actively addressing cross-domain discrepancies.Additionally,it integrates an attention-gated mechanism that generates adaptive adjacency matrices to flexibly model collaborative patterns among facial landmarks.While largely preserving the structural paradigm of SASTB,GDSTB enhances the feature representation by integrating global context extracted from a pretrained model.Through this dual-branch architecture,CDST-GCN success-fully models both the global and local spatial-temporal features.The experimental results on CASME II and SAMM datasets demonstrate that the proposed model achieves competitive perfor-mance.Especially in more challenging 5-class tasks,the accuracy of the model on CASME II dataset is as high as 80.5%.
文摘Micro-expressions,fleeting involuntary facial cues lasting under half a second,reveal genuine emotions and are valuable in clinical diagnosis and psychotherapy.Real-time recognition on resource-constrained embedded devices remains challenging,as current methods struggle to balance performance and efficiency.This study introduces a semi-lightweight multifunctional network that enhances real-time deployment and accuracy.Unlike prior simplistic feature fusion techniques,our novel multi-feature fusion strategy leverages temporal,spatial,and differential features to better capture dynamic changes.Enhanced by Residual Network(ResNet)architecture with channel and spatial attention mechanisms,the model improves feature representation while maintaining a lightweight design.Evaluations on SMIC,CASME II,SAMM,and their composite dataset show superior performance in Unweighted F1 Score(UF1)and Unweighted Average Recall(UAR),alongside faster detection speeds compared to existing algorithms.
基金Supported by Shaanxi Province Key Research and Development Project (2021GY-280)the National Natural Science Foundation of China (No.61834005,61772417,61802304)。
文摘Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expression recognition algorithm is proposed based on graph convolution network(GCN) and Transformer model.Firstly, action unit(AU) feature detection is extracted and facial muscle nodes in the neighborhood are divided into three subsets for recognition.Then, graph convolution layer is used to find the layout of dependencies between AU nodes of micro-expression classification.Finally, multiple attentional features of each facial action are enriched with Transformer model to include more sequence information before calculating the overall correlation of each region.The proposed method is validated in CASME II and CAS(ME)^2 datasets, and the recognition rate reached 69.85%.
基金supported by the National Natural Science Foundation of Hunan Province,China(Grant Nos.2021JJ50058,2022JJ50051)the Open Platform Innovation Foundation of Hunan Provincial Education Department(Grant No.20K046)The Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.21A0350,21C0439,19A133).
文摘Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction.Based on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is proposed.In the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped IBs.To alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,respectively.In the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be extracted.Finally,after fusing the global and local feature vectors,the ME time-series information is extracted by Bi-LSTM.The experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.
基金the National Natural Science Foundation of China(Nos.61772417,61634004,and 61602377)the Key R&D Progrm Projects in Shaanxi Province(No.2017GY-060)the Shaanxi Natural Science Basic Research Project(No.018JM4018)。
文摘The intensity of the micro-expression is weak,although the directional low frequency components in the image are preserved by many algorithms,the extracted micro-expression ft^ature information is not sufficient to accurately represent its sequences.In order to improve the accuracy of micro-expression recognition,first,each frame image is extracted from,its sequences,and the image frame is pre-processed by using gray normalization,size normalization,and two-dimensional principal component analysis(2DPCA);then,the optical flow method is used to extract the motion characteristics of the reduced-dimensional image,the information entropy value of the optical flow characteristic image is calculated by the information entropy principle,and the information entropy value is analyzed to obtain the eigenvalue.Therefore,more micro-expression feature information is extracted,including more important information,which can further improve the accuracy of micro-expression classification and recognition;finally,the feature images are classified by using the support vector machine(SVM).The experimental results show that the micro-expression feature image obtained by the information entropy statistics can effectively improve the accuracy of micro-expression recognition.
基金Shaanxi Province Key Research and Development Project(No.2021 GY-280)Shaanxi Province Natural Science Basic Research Program Project(No.2021JM-459)+1 种基金National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61634004)Shaanxi Province International Science and Technology Cooperation Project(No.2018KW-006)。
文摘Aiming at the problem of unsatisfactory effects of traditional micro-expression recognition algorithms,an efficient micro-expression recognition algorithm is proposed,which uses convolutional neural networks(CNN)to extract spatial features of micro-expressions,and long short-term memory network(LSTM)to extract time domain features.CNN and LSTM are combined as the basis of micro-expression recognition.In many CNN structures,the visual geometry group(VGG)using a small convolution kernel is finally selected as the pre-network through comparison.Due to the difficulty of deep learning training and over-fitting,the dropout method and batch normalization method are used to solve the problem in the VGG network.Two data sets CASME and CASME II are used for test comparison,in order to solve the problem of insufficient data sets,randomly determine the starting frame,and a fixedlength frame sequence is used as the standard,and repeatedly read all sample frames of the entire data set to achieve trayersal and data amplification.Finallv.a hieh recognition rate of 67.48% is achieved.
基金Supported by the Shaanxi Province Key Research and Development Project(No.2021GY-280)Shaanxi Province Natural Science Basic Re-search Program Project(No.2021JM-459)+1 种基金the National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61634004)the Shaanxi Province International Science and Technology Cooperation Project(No.2018KW-006).
文摘The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.
文摘Background The use of micro-expression recognition to recognize human emotions is one of the most critical challenges in human-computer interaction applications. In recent years, cross-database micro-expression recognition(CDMER) has emerged as a significant challenge in micro-expression recognition and analysis. Because the training and testing data in CDMER come from different micro-expression databases, CDMER is more challenging than conventional micro-expression recognition. Methods In this paper, an adaptive spatio-temporal attention neural network(ASTANN) using an attention mechanism is presented to address this challenge. To this end, the micro-expression databases SMIC and CASME II are first preprocessed using an optical flow approach,which extracts motion information among video frames that represent discriminative features of micro-expression.After preprocessing, a novel adaptive framework with a spatiotemporal attention module was designed to assign spatial and temporal weights to enhance the most discriminative features. The deep neural network then extracts the cross-domain feature, in which the second-order statistics of the sample features in the source domain are aligned with those in the target domain by minimizing the correlation alignment(CORAL) loss such that the source and target databases share similar distributions. Results To evaluate the performance of ASTANN, experiments were conducted based on the SMIC and CASME II databases under the standard experimental evaluation protocol of CDMER. The experimental results demonstrate that ASTANN outperformed other methods in relevant crossdatabase tasks. Conclusions Extensive experiments were conducted on benchmark tasks, and the results show that ASTANN has superior performance compared with other approaches. This demonstrates the superiority of our method in solving the CDMER problem.
基金This work is funded by the natural science foundation of Jiangsu Province(No.BK20150471)the natural science foundation of the higher education institutions of Jiangsu Province(No.17KJB520007)+2 种基金the Key Research and Development Program of Zhenjiang-Social Development(No.SH2018005)the scientific researching fund of Jiangsu University of Science and Technology(No.1132921402,No.1132931803)the basic science and frontier technology research program of Chongqing Municipal Science and Technology Commission(cstc2016jcyjA0407).
文摘Micro-expression recognition has attracted growing research interests in the field of compute vision.However,micro-expression usually lasts a few seconds,thus it is difficult to detect.This paper presents a new framework to recognize micro-expression using pyramid histogram of Centralized Gabor Binary Pattern from Three Orthogonal Panels(CGBP-TOP)which is an extension of Local Gabor Binary Pattern from Three Orthogonal Panels feature.CGBP-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image sequences.In order to keep more local information of the face,CGBP-TOP is extracted based on pyramid subregions of the micro-expression video frame.The combination of CGBP-TOP and spatial pyramid can represent well and truly the facial movements of the micro-expression image sequences.However,the dimension of our pyramid CGBP-TOP tends to be very high,which may lead to high data redundancy problem.In addition,it is clear that people of different genders usually have different ways of micro-expression.Therefore,in this paper,in order to select the relevant features of micro-expression,the gender-specific sparse multi-task learning method with adaptive regularization term is adopted to learn a compact subset of pyramid CGBP-TOP feature for micro-expression classification of different sexes.Finally,extensive experiments on widely used CASME II and SMIC databases demonstrate that our method can efficiently extract micro-expression motion features in the micro-expression video clip.Moreover,our proposed approach achieves comparable results with the state-of-the-art methods.
基金Authors would like to acknowledge funding from Universiti Kebangsaan Malaysia(Geran Universiti Penyelidikan:GUP-2019-008 and Dana Padanan Kolaborasi:DPK-2021-012).
文摘Micro-expression is manifested through subtle and brief facial movements that relay the genuine person’s hidden emotion.In a sequence of videos,there is a frame that captures the maximum facial differences,which is called the apex frame.Therefore,apex frame spotting is a crucial sub-module in a micro-expression recognition system.However,this spotting task is very challenging due to the characteristics of micro-expression that occurs in a short duration with low-intensity muscle movements.Moreover,most of the existing automated works face difficulties in differentiating micro-expressions from other facial movements.Therefore,this paper presents a deep learning model with an attention mechanism to spot the micro-expression apex frame from optical flow images.The attention mechanism is embedded into the model so that more weights can be allocated to the regions that manifest the facial movements with higher intensity.The method proposed in this paper has been tested and verified on two spontaneous micro-expression databases,namely Spontaneous Micro-facial Movement(SAMM)and Chinese Academy of Sciences Micro-expression(CASME)II databases.The proposed system performance is evaluated by using the Mean Absolute Error(MAE)metric that measures the distance between the predicted apex frame and the ground truth label.The best MAE of 14.90 was obtained when a combination of five convolutional layers,local response normalization,and attention mechanism is used to model the apex frame spotting.Even with limited datasets,the results have proven that the attention mechanism has better emphasized the regions where the facial movements likely to occur and hence,improves the spotting performance.
基金supported by the Science and Technology Development Fund of Macao,China(No.0035/2023/ITP1)the National Natural Science Foundation of China(No.62076122)+2 种基金the Basic Science(Natural Science)Research Project of Higher Education Institutions in Jiangsu Province(No.24KJA520003)the 333 High-Level Talents in Jiangsu Province(2024)the Fundamental Research Funds for the Central Universities(No.2242024k30027).
文摘As mobile devices and sensor technology advance,their role in communication becomes increasingly indispensable.Micro-expression recognition,an invaluable non-verbal communication method,has been extensively studied in human-computer interaction,sentiment analysis,and security fields.However,the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods,raising concerns about serious privacy leakage and data sharing.To address these limitations,we investigate a federated learning scheme tailored specifically for this task.Our approach prioritizes user privacy by employing federated optimization techniques,enabling the aggregation of clients’knowledge in an encrypted space without compromising data privacy.By integrating established micro-expression recognition methods into our framework,we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms.To our knowledge,this marks the first application of federated learning to the micro-expression recognition task.
基金supported by the Science and Technology Development Fund of Macao(No.0035/2023/ITP1)the National Natural Science Foundation of China(Nos.U1836220 and 61672267)+2 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX19_1616)the Qing Lan Talent Program of Jiangsu ProvinceJiangsu Province Key Research and Development Plan(Industry Foresight and Key Core Technology)-Competitive Project(No.BE2020036).
文摘Micro-Expression Recognition(MER)is a challenging task as the subtle changes occur over different action regions of a face.Changes in facial action regions are formed as Action Units(AUs),and AUs in micro-expressions can be seen as the actors in cooperative group activities.In this paper,we propose a novel deep neural network model for objective class-based MER,which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks(GCN).Specifically,we propose two new strategies in our AU detection module for more effective AU feature learning:the attention mechanism and the balanced detection loss function.With these two strategies,features are learned for all the AUs in a unified model,eliminating the error-prune landmark detection process and tedious separate training for each AU.Moreover,our model incorporates a tailored objective class-based AU knowledge-graph,which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation.Extensive experiments on two tasks in MEGC 2018 show that our approach outperforms the current state-of-the-art methods in MER.Additionally,we also report our single model-based micro-expression AU detection results.
基金supported by the NSFC–Zhejiang Joint Fund of the Integration of Informatization and Industrialization under Grant No.U1909210the the National Natural Science Foundation of China under Grant No.61772312the Fundamental Research Funds of Shandong University(Grant No.2018JC030)。
文摘Micro-expression recognition is a substantive cross-study of psychology and computer science,and it has a wide range of applications(e.g.,psychological and clinical diagnosis,emotional analysis,criminal investigation,etc.).However,the subtle and diverse changes in facial muscles make it difficult for existing methods to extract effective features,which limits the improvement of micro-expression recognition accuracy.Therefore,we propose a multi-scale joint feature network based on optical flow images for micro-expression recognition.First,we generate an optical flow image that reflects subtle facial motion information.The optical flow image is then fed into the multi-scale joint network for feature extraction and classification.The proposed joint feature module(JFM)integrates features from different layers,which is beneficial for the capture of micro-expression features with different amplitudes.To improve the recognition ability of the model,we also adopt a strategy for fusing the feature prediction results of the three JFMs with the backbone network.Our experimental results show that our method is superior to state-of-the-art methods on three benchmark datasets(SMIC,CASME II,and SAMM)and a combined dataset(3 DB).
文摘Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress,hide,disguise,or conceal.Such expressions can reflect a person's real emotions and have a wide range of application in public safety and clinical diagnosis.The analysis of facial micro-expressions in video sequences through computer vision is still relatively recent.In this research,a comprehensive review on the topic of spotting and recognition used in micro expression analysis databases and methods,is conducted,and advanced technologies in this area are summarized.In addition,we discuss challenges that remain unresolved alongside future work to be completed in the field of micro-expression analysis.
文摘Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.
基金supported by the National Natural Science Foundation of China(No.62102180)the Research Grants Council of Hong Kong(Collaborative Research Fund No.C7055-21GF)the Hong Kong Scholars Program,and the Natural Science Foundation of Jiangsu Province(No.BK20210329).
文摘Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or fabricated.Micro-expression recognition(MER)is one of the most challenging topics in affective computing.It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period.Recently,many deep learning-based MER methods have been developed.However,how to effectively capture subtle temporal variations for robust MER still perplexes us.We propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based MER.To explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t.original ME sequences and the counterfactuals w.r.t.counterfactually-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues.Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art methods.The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.
基金supported in part by the National Natural Science Foundation of China:61773330.
文摘Audio-visual speech recognition(AVSR),which integrates audio and visual modalities to improve recognition performance and robustness in noisy or adverse acoustic conditions,has attracted significant research interest.However,Conformer-based architectures remain computational expensive due to the quadratic increase in the spatial and temporal complexity of their softmax-based attention mechanisms with sequence length.In addition,Conformerbased architectures may not provide sufficient flexibility for modeling local dependencies at different granularities.To mitigate these limitations,this study introduces a novel AVSR framework based on a ReLU-based Sparse and Grouped Conformer(RSG-Conformer)architecture.Specifically,we propose a Global-enhanced Sparse Attention(GSA)module incorporating an efficient context restoration block to recover lost contextual cues.Concurrently,a Grouped-scale Convolution(GSC)module replaces the standard Conformer convolution module,providing adaptive local modeling across varying temporal resolutions.Furthermore,we integrate a Refined Intermediate Contextual CTC(RIC-CTC)supervision strategy.This approach applies progressively increasing loss weights combined with convolution-based context aggregation,thereby further relaxing the constraint of conditional independence inherent in standard CTC frameworks.Evaluations on the LRS2 and LRS3 benchmark validate the efficacy of our approach,with word error rates(WERs)reduced to 1.8%and 1.5%,respectively.These results further demonstrate and validate its state-of-the-art performance in AVSR tasks.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128 and 62306139the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition.However,existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds,small target objects,and limited training data,leading to poor recognition.Fine-grained images exhibit“small inter-class differences,”and while second-order feature encoding enhances discrimination,it often requires dual Convolutional Neural Networks(CNN),increasing training time and complexity.This study proposes a model integrating discriminative region localization and efficient second-order feature encoding.By ranking feature map channels via a fully connected layer,it selects high-importance channels to generate an enhanced map,accurately locating discriminative regions.Cropping and erasing augmentations further refine recognition.To improve efficiency,a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers(ResNet-50)and multiplies it with features from the fifth group,producing second-order features while reducing dimensionality and training time.Experiments on Caltech-University of California,San Diego Birds-200-2011(CUB-200-2011),Stanford Car,and Fine-Grained Visual Classification of Aircraft(FGVC Aircraft)datasets show state-of-the-art accuracy of 88.9%,94.7%,and 93.3%,respectively.
