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
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
The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensit...The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensity changes,which can compromise accuracy and repeatability.In this study,we report a novel fluorescent probe(R)-Z1 that achieves effective enantioselective recognition of chiral amino acids in water by altering emission wavelengths(>60 nm).This water-soluble probe(R)-Z1 exhibits cyan or yellow-green luminescence upon interaction with amino acid enantiomers,enabling reliable chiral detection of 14 natural amino acids.It also allows for the determination of enantiomeric excess through monitoring changes in luminescent color.Additionally,a logic operation with two inputs and three outputs was constructed based on these optical properties.Notably,amino acid enantiomers were successfully detected via dual-channel analysis at both the food and cellular levels.This study provides a new dynamic luminescence-based tool for the accurate sensing and detection of amino acid enantiomers.展开更多
Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patient...Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.展开更多
Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.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.
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the financial support from the National Natural Science Foundation of China(Nos.22377097,22307036,22074114)Natural Science Foundation of Hubei Province of China(Nos.2020CFB623,2021CFB556)Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education(No.LCX202305)。
文摘The detection of amino acid enantiomers holds significant importance in biomedical,chemical,food,and other fields.Traditional chiral recognition methods using fluorescent probes primarily rely on fluorescence intensity changes,which can compromise accuracy and repeatability.In this study,we report a novel fluorescent probe(R)-Z1 that achieves effective enantioselective recognition of chiral amino acids in water by altering emission wavelengths(>60 nm).This water-soluble probe(R)-Z1 exhibits cyan or yellow-green luminescence upon interaction with amino acid enantiomers,enabling reliable chiral detection of 14 natural amino acids.It also allows for the determination of enantiomeric excess through monitoring changes in luminescent color.Additionally,a logic operation with two inputs and three outputs was constructed based on these optical properties.Notably,amino acid enantiomers were successfully detected via dual-channel analysis at both the food and cellular levels.This study provides a new dynamic luminescence-based tool for the accurate sensing and detection of amino acid enantiomers.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
