Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks472 from public data
- 2,837
- 1,024
- CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation990
An improved micro-expression recognition system, CASME II, is built, with higher temporal resolution (200 fps) and spatial resolution (about 280×340 pixels on facial area) and elicited participants' facial expressions in a well-controlled laboratory environment and proper illumination.
- Facial expression recognition from near-infrared videos778
A novel research on a dynamic facial expression recognition, using near-infrared (NIR) video sequences and LBP-TOP feature descriptors and component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial expressions.
- Computer Vision Using Local Binary Patterns772
Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains and provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems.
- 668
- Searching Central Difference Convolutional Networks for Face Anti-Spoofing637
A novel frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information is proposed.
- 550
- Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond527
The Aff-Wild benchmark for training and evaluating affect recognition algorithms is introduced and an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues is designed and extensively train.
- 520
- 457
- 410
- 406
- 385
- From BoW to CNN: Two Decades of Texture Representation for Texture Classification375
A comprehensive survey of advances in texture representation over the last two decades is presented covering different aspects of the research, including benchmark datasets and state of the art results.
- Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching371
The first automatically designed GCN for skeleton-based action recognition is proposed and enriched by providing multiple dynamic graph modules after fully exploring the spatial-temporal correlations between nodes, and a sampling- and memory-efficient evolution strategy is proposed to search an optimal architecture for this task.
- PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer343
This paper proposes the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement, and proposes the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain.
- Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement341
Comprehensive experiments are performed to show that the proposed method not only achieves superior performance on compressed videos with high-quality videos pair, but also generalizes well on novel data with only compressed videos available, which implies the promising potential for real-world applications.
- 309
- Spontaneous facial micro-expression analysis using Spatiotemporal Completed Local Quantized Patterns303
Experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions, and an efficient vector quantization and discriminative codebook selection is proposed to make LBP-TOP more discrim inative and compact.
- Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions290
A novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro- expression sequence and optimized by an end-to-end manner and obviates manual feature design is proposed.
- Deep Learning for Face Anti-Spoofing: A Survey275
This paper presents the first comprehensive review of recent advances in deep learning based FAS and collects and analyze the latest methods specially designed for domain generalization and open-set FAS.
- NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing275
This work proposes the first FAS method based on neural architecture search (NAS), called NAS-FAS, to discover the well-suited task-aware networks, and develops a novel search space consisting of central difference convolution and pooling operators.
- 246
- Video-Based Remote Physiological Measurement via Cross-Verified Feature Disentangling244
A cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations, and then use the distilled physiological features for robust multi-task physiological measurements.
- 3D Mask Face Anti-spoofing with Remote Photoplethysmography218
A novel approach to 3D mask face anti-spoofing from a new perspective is proposed, by analysing heartbeat signal through remote Photoplethysmography (rPPG) to extract discriminative local heartbeat signal patterns so that an imposter can better be detected regardless of the material and quality of the mask.
- Revealing the Invisible With Model and Data Shrinking for Composite-Database Micro-Expression Recognition216
It is discovered that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task and a recurrent convolutional network (RCN) is proposed to explore the Shallower-Architecture and lower- resolution input data, shrinking model and input complexities simultaneously.
- 212
- 209
- Discriminative features for texture description208
A feature extraction method for texture description is developed which can be integrated with existing LBP variants such as conventional LBP, rotation invariant patterns, local patterns with anisotropic structure, completed local binary pattern (CLBP) and local ternary pattern (LTP) to derive new image features for texture classification.
- Local Binary Patterns for Still Images206
This chapter provides an in-depth description of the LBP operator in spatial image domain, and its rotation-invariant and multiscale versions are introduced.
- 203
- Human Activity Recognition Using a Dynamic Texture Based Method196
The method uses dynamic texture descriptors to describe human movements in a spatiotemporal way and works on image data rather than silhouettes, following recent trends in computer vision research.
- Discriminative Spatiotemporal Local Binary Pattern with Revisited Integral Projection for Spontaneous Facial Micro-Expression Recognition194
A discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition and a new feature selection based on Laplacian method for increasing the discrimination of micro-expressions is proposed.
- 193
- A review of recent advances in visual speech decoding187
A detailed review of recent advances in visual speech decoding, focusing on the important questions asked by researchers and summarize the recent studies that attempt to answer them, and providing details of audio-visual speech databases.
- 170
- 165
- 164
- Face Anti-Spoofing with Human Material Perception161
The Bilateral Convolutional Networks (BCN) is proposed, which is able to capture intrinsic material-based patterns via aggregating multi-level bilateral macro- and micro- information and Multi-level Feature Refinement Module (MFRM) and multi-head supervision are utilized to learn more robust features.
- PhysFormer++: Facial Video-Based Physiological Measurement with SlowFast Temporal Difference Transformer160
This paper proposes two end-to-end video transformer based architectures, namely PhysFormer and PhysFormer++, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement, and proposes the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain.
- 160
- 159
- Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition158
A comprehensive evaluation of the proposed spatio-temporal transformer architecture shows that the proposed approach consistently outperforms the state of the art, and is the first framework in the published literature on micro-expression recognition to achieve the unweighted F1-score greater than 0.9.
- Deep Learning for Micro-Expression Recognition: A Survey157
This survey provides a comprehensive review of deep MER methods and defines a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods.
- 151
- 151
- 150
- Boosted multi-resolution spatiotemporal descriptors for facial expression recognition145
The LBP-TOP features are extended to multi-resolution spatiotemporal space and used for describing facial expressions and AdaBoost is utilized to learn the principal appearance and motion and for selecting the most important expression-related features.
- 142
- iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis140
iMiGUE is designed to evaluate the ability of models to analyze the emotional states by integrating information of recognized micro-gesture, rather than just recognizing prototypes in the sequences separately (or isolatedly), and an unsupervised learning method is proposed to capture latent representations from the micro- gesture sequences themselves.
- TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection140
This letter proposes a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently and is lightweight and efficient, which is promising for mobile-level applications.
- Extended local binary patterns for face recognition139
A labeled dominant pattern scheme is proposed to learn salient information that impressively outperforms other well-known systems with a recognition rate of 74.6% on the CAS-PEAL-R1 lighting probe set and highly robust to illumination variations.
- Dual-Cross Central Difference Network for Face Anti-Spoofing137
Two Cross Central Difference Convolutions are proposed, which exploit the difference of the center and surround sparse local features from the horizontal/vertical and diagonal directions, respectively, and a novel Patch Exchange (PE) augmentation strategy for FAS is proposed via simply exchanging the face patches as well as their dense labels from random samples.
- Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition134
The proposed method includes two key components: enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities.
- Dynamic Texture Recognition Using Volume Local Binary Patterns129
This paper proposes a new method for recognizing dynamic textures modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in still texture analysis, combining the motion and appearance together.
- Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video127
A real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation that can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model.
- 123
- 121
- 120
- 119
- 117
- Multi-modal emotion analysis from facial expressions and electroencephalogram116
The experimental results and comparisons with the average human performance show the effectiveness of the proposed multi-modal approach, based on facial expression and electroencephalogram (EEG) technologies.
- MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning114
This high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.
- Micro-Expression Recognition Using Robust Principal Component Analysis and Local Spatiotemporal Directional Features114
This paper uses the sparse part of Robust PCA (RPCA) to extract the subtle motion information of micro-expression and the proposed method may further be used to extract other subtlemotion information from video.
- SRN: Side-Output Residual Network for Object Symmetry Detection in the Wild110
A new benchmark and an end-to-end deep learning approach are presented, opening up a promising direction for symmetry detection in the wild, and the proposed symmetry detection approach, named Side-output Residual Network (SRN), exploits the flow of errors among multiple scales to ease the problems of fitting complex outputs with limited layers.
- Revisiting Pixel-Wise Supervision for Face Anti-Spoofing109
A novel pyramid supervision is proposed, which guides deep models to learn both local details and global semantics from multi-scale spatial context in face anti-spoofing, to improve the performance beyond existing pixel-wise supervision frameworks and enhance the model’s interpretability.
- 109
- Dynamic Facial Expression Recognition Using Longitudinal Facial Expression Atlases109
The proposed method was extensively evaluated on the Cohn-Kanade, MMI, and Oulu-CASIA VIS dynamic facial expression databases and was compared with several state-of-the-art facial expression recognition approaches.
- 108
- 107
- The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress107
This paper utilises the MuSe-CaR dataset focusing on user-generated reviews and introduces the Ulm-TSST dataset, which displays people in stressful depositions, and provides detail on the state-of-the-art feature sets extracted from these datasets for utilisation by the baseline model, a Long Short-Term Memory-Recurrent Neural Network.
- SMG: A Micro-gesture Dataset Towards Spontaneous Body Gestures for Emotional Stress State Analysis105
A comprehensive study of micro-gestures, used in the psychology research field to interpret inner human feelings, is presented, including a novel spontaneous micro-gesture dataset with two emotional stress states and a comprehensive statistical analysis indicating the correlations between MGs and emotional states.
- Texture Classification using a Linear Configuration Model based Descriptor105
The descriptor MiC is shown that encodes image microscopic configuration by a linear configuration model that could avoid the generalization problem suffered by other statistical learning methods.
- 101
- Dynamic texture and scene classification by transferring deep image features101
Inspired by the success of deep structures in image classification, a well-trained Convolutional Neural Network is applied as a feature extractor to extract mid-level features from each frame, and then form the video-level representation by concatenating the first and the second order statistics over the mid- level features.
- From Emotion AI to Cognitive AI100
The current development in emotion AI is reviewed, the concept of cognitive AI is introduced, and the envisioned future of cognitiveAI, which intends to let computers think, reason, and make decisions in similar ways that humans do is proposed.
- Recognition of human actions using texture descriptors98
This paper adopts the ideas of spatiotemporal analysis and the use of local features for motion description and uses temporal templates to capture movement dynamics and then uses texture features to characterize the observed movements.
- Modality Unifying Network for Visible-Infrared Person Re-Identification96
A novel Modality Unifying Network (MUN) is proposed to explore a robust auxiliary modality for VI-ReID and surpasses the current state-of-the-art methods by a significant margin.
- Multi-Modal Face Anti-Spoofing Based on Central Difference Networks96
The central difference convolutional networks (CDCN) is extended to a multimodal version, intending to capture intrinsic spoofing patterns among three modalities (RGB, depth and infrared), and an elaborate study about singlemodal based CDCN is given.
- Isolated Sign Language Recognition with Grassmann Covariance Matrices94
This article proposes a covariance matrix--based representation to naturally fuse information from multimodal sources to utilize long-term dynamics over an isolated sign sequence, and demonstrates that the proposed method outperforms the state-of-the-art methods both in accuracy and computational cost.
- RLBP: Robust Local Binary Pattern94
This paper proposes a simple and robust local descriptor, called the robust local binary pattern (RLBP), which impressively outperforms the other widely used descriptors and other variants of LBP, and shows a promising performance on the Face Recognition Grand Challenge (FRGC) face dataset.
- A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images93
The experimental results show that the proposed location-to-segmentation strategy achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly.
- Face Liveness Detection by rPPG Features and Contextual Patch-Based CNN88
A generalized method exploiting both rPPG and texture features for face anti-spoofing task by exploiting multi-scale long-term statistical spectral features with variant granularities and a contextual patch-based convolutional neural network.
- 88
- 87
- Facial Micro-Expressions: An Overview84
This survey provides a comprehensive review of ME development in the field of computer vision, from the ME studies in psychology and early attempts in computer vision to various computational ME analysis methods and future directions.
- Two decades of local binary patterns84
In this chapter, the most recent and important variants of LBP in 2- D, spatiotemporal, 3-D, and 4-D domains are surveyed and some future challenges for research are presented.
- 82
- 81
- 80
- 4DME: A Spontaneous 4D Micro-Expression Dataset With Multimodalities79
The 4DME dataset is introduced: a new spontaneous ME dataset which includes 4D data along with three other video modalities which could potentially benefit ME recognition and the importance of forming a clear and unified criteria of ME annotation for future ME data collection studies is emphasized.
- Cross-Database Micro-Expression Recognition: A Benchmark78
A CDMER experimental evaluation protocol is established and a standard platform for evaluating their proposed methods is provided and extensive benchmark experiments are conducted by using NINE state-of-the-art domain adaptation (DA) methods and SIX popular spatiotemporal descriptors for investigating the CDMER problem from two different perspectives.
- A hybrid quantum–classical neural network with deep residual learning74
A novel hybrid quantum-classical neural network with deep residual learning (Res-HQCNN) is proposed, which performs better to learn an unknown unitary transformation and has stronger robustness for noisy data, when compared to state of the arts.
- MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition72
This year's MER2024, besides expanding the dataset size, is further introduced a new track around open-vocabulary emotion recognition, which encourages participants to generate any number of labels in any category, aiming to describe emotional states as accurately as possible.
- Spontaneous micro-expression spotting via geometric deformation modeling72
A probabilistic framework to detect spontaneous micro-expression clips temporally from a video sequence (micro-expression spotting) and a random walk model is presented to calculate the probability of individual frames having micro-expressions is presented.
- 70
- 69
- Spatial Temporal Graph Deconvolutional Network for Skeleton-Based Human Action Recognition68
Extensive experiments verify that ST-GDN consistently improves the performance and largely reduce the model size on these datasets, and provides a better message aggregation by removing the embedding redundancy of the input graphs from either node-wise, frame-wise or element-wise at different network layers.
- Micro-expression action unit detection with spatial and channel attention68
This work proposes a novel micro-expression AU detection method by utilizing self high-order statistics of spatio-wise and channel-wise features which can be considered as spatial and channel attentions, respectively, and expects to utilize rich relationship information of facial regions to increase the AU detection robustness on limited micro-expression samples.
- Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition67
A novel spatial-temporal GCN (ST-GCN) architecture which is defined via the Poincaré geometry such that it is able to better model the latent anatomy of the structure data and could achieve a superior performance under any given evaluation metrics.
- 67
- 63
- Spatiotemporal Integration of Optical Flow Vectors for Micro-expression Detection62
An algorithm to detect micro-expressions by utilizing the motion features to capture direction continuity and computes the optical flow vector for small local spatial regions and integrates them in local spatiotemporal regions is proposed.
- Towards a dynamic expression recognition system under facial occlusion62
This work proposes a complete system consisting of facial representations, occlusion detection, and multiple feature fusion in video sequences, and presents an evaluation demonstrating that the proposed facial representation is independent of facial identity.
- Dynamic Texture Based Gait Recognition62
A novel approach for human gait recognition that inherently combines appearance and motion is presented and a new coding of multiresolution uniform Local Binary Patterns is proposed and used in the construction of spatiotemporal LBP histograms.
- 62
- Rethinking the ST-GCNs for 3D skeleton-based human action recognition61
A simple but efficient strategy to capture the global graph correlations and thus model the representation of the input graph sequences is provided and a multi-scale temporal strategy is introduced to efficiently capture the dynamic information.
- 61
- 60
- Surveillance Face Anti-Spoofing59
A Contrastive Quality-Invariance Learning (CQIL) network is proposed to alleviate the performance degradation caused by image quality from three aspects: an Image Quality Variable module (IQV), an Image Quality Variable module (IQV), and a Separate Quality Network (SQN) designed to learn discriminative features independent of image quality.
- 58
- Tripool: Graph triplet pooling for 3D skeleton-based action recognition57
Tripool is proposed, a novel graph pooling method for 3D action recognition from skeleton data that overcomes the limitation of the topology constrain for the high-level semantic representations, thus improves the performance.
- Micro-expression spotting: A new benchmark57
An extension of the SMIC-E database is introduced, namely the SMic-E-Long database, which is a new challenging benchmark for ME spotting and a new evaluation protocol is suggested that standardizes the comparison of various ME spotting techniques.
- 57
- Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection56
This paper designs a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map, and formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-predicted, from the coarse scale to the finer scale.
- Sparse tensor canonical correlation analysis for micro-expression recognition56
Experimental results show that STCCA performs better than the Three-dimensional Canonical Correlation Analysis (3D-CCA) without sparse resolution, and the experiments also showed that Multi-linear Principal Component Analysis (MPCA) is not suitable for micro-expression recognition because the eigenvectors corresponding to smaller eigenvctors are discarded, and those eigenvesctors include brief and subtle motion information.
- 56
- Deep-HR: Fast heart rate estimation from face video under realistic conditions55
A simple yet efficient approach to benefit the advantages of the Deep Neural Network by simplifying HR estimation from a complex task to learning from very correlated representation to HR is proposed.
- Carbon Dioxide Sorbents with Propylamine Groups−Silica Functionalized with a Fractional Factorial Design Approach55
Experimental design is a time-efficient approach to the functionalization of silica with propylamine groups, and certain two-way interactions were shown to be statistically significant as well.
- 54
- Quantifying Micro-expressions with Constraint Local Model and Local Binary Pattern54
Two feature extraction methods to analyze micro-expressions by assessing the dynamic information show good performance, with sensitivity and reliability, and would assist behavior researchers in measuring facial movements on various facets and at a deeper level.
- 53
- Concatenated Frame Image Based CNN for Visual Speech Recognition52
A novel sequence image representation method called concatenated frame image (CFI), two types of data augmentation methods for CFI, and a framework of CFI-based convolutional neural network (CNN) for visual speech recognition (VSR) task are proposed.
- Uncertain Facial Expression Recognition via Multi-Task Assisted Correction51
A novel method of multi-task assisted correction in addressing uncertain facial expression recognition called MTAC, which substantially improves over baselines when facing synthetic and real uncertainties and outperforms the state-of-the-art methods.
- Flexible-Modal Face Anti-Spoofing: A Benchmark51
The first flexible-modal FAS benchmark is established with the principle ‘train one for all’ and it is hoped this new benchmark will facilitate the future research of the multi-modAL FAS.
- Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition51
This paper proposes a simple yet effective method called Target Sample Re-Generator (TSRG), which is able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples.
- 49
- 48
- 48
- Robust local features for remote face recognition47
Experimental results show promising results for two challenging datasets which have poor image quality, i.e., a remote face dataset and the Point and Shoot Face Recognition Challenge dataset.
- Reading Hidden Emotions: Spontaneous Micro-expression Spotting and Recognition.46
This work proposes the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data, and shows that this method achieves comparable performance to humans at this very challenging task, and outperforms humans in the ME recognition task by a large margin.
- Descriptor Learning Based on Fisher Separation Criterion for Texture Classification46
A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intraclass similarity and inter-class distance and it is found that such a learning technique can largely improve the discriminative ability and automatically achieve a good tradeoff between discrim inative power and efficiency.
- 44
- 43
- A spatial-aware joint optic disc and cup segmentation method43
A spatial-aware joint segmentation method by explicitly considering the spatial locations of the pixels and learning the multi-scale spatially dense features is proposed, and high correlation between the cup-to-disk values and the risks of the glaucoma is validated on the dataset ORIGA.
- 43
- Graph-Based Facial Affect Analysis: A Review42
A comprehensive review of graph-based FAA methods, including the evolution of algorithms and their applications, and a trend towards graph construction is shown, which can serve as a reference for future research in this field.
- 42
- TEXTURE BASED DESCRIPTION OF MOVEMENTS FOR ACTIVITY ANALYSIS42
This paper proposes a novel approach for activity analysis by describing human activities with texture features by extracts spatially enhanced local binary pattern (LBP) histograms from temporal templates and models their temporal behavior with hidden Markov models.
- Video Action Recognition Via Neural Architecture Searching40
This paper makes the first attempt to let algorithm automatically design neural networks for video action recognition tasks using a spatio-temporal network developed in a differentiable space modeled by a directed acyclic graph.
- 40
- Capturing correlations of local features for image representation40
The superiority of the second-order standardized moment average pooling (2Standmap) is suggested and 2Standmap is successfully applied to four challenging tasks namely texture classification, medical image analysis, pain expression recognition, and micro-expression recognition.
- 39
- Projective testing of diurnal collective emotion39
This paper develops and validate in controlled conditions a quantitative projective test that can reveal emotions and demonstrates that this test can be deployed on a large scale longitudinally: a four-week deployment in the authors' university's public spaces where 1431 tests were completed anonymously by passers-by.
- A Survey of Recent Advances in Texture Representation.38
A comprehensive survey of advances in texture representation over the last two decades is presented and discusses open challenges and directions for future research.
- 38
- Globally rotation invariant multi-scale co-occurrence local binary pattern37
The proposed MCLBP performs very well on texture, material, and medical cell classification and is also lower than that of the CLBP_S/M/C and LBPHF_S_M.
- Riesz-based Volume Local Binary Pattern and A Novel Group Expression Model for Group Happiness Intensity Analysis37
A new method to estimate the happiness intensity of a group of people in an image is proposed, which combines the Riesz transform and the local binary pattern descriptor, namedRiesz-based volume local binary patterns, which considers neighbouring changes not only in the spatial domain of a face but also along the different RiesZ faces.
- 35
- 35
- 35
- Cross-domain heterogeneous residual network for single image super-resolution34
The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance.
- Improved Spatiotemporal Local Monogenic Binary Pattern for Emotion Recognition in The Wild34
An improved spatiotemporal feature descriptor based on STLMBP is proposed, which uses not only magnitude and orientation, but also the phase information, which provide complementary information in the robustness of LBP-TOP to unconstrained environment.
- Signal Reconstruction of Compressed Sensing Based on Alternating Direction Method of Multipliers33
An algorithm based on the alternating direction method of multipliers is proposed, which shows that a higher signal reconstruction success probability is obtained when compared to existing methods, while a low computational cost is required.
- 33
- 33
- 33
- 33
- Local spatiotemporal descriptors for visual recognition of spoken phrases33
Local spatiotemporal descriptors are proposed to represent and recognize spoken isolated phrases based solely on visual input to include local processing and robustness to monotonic gray-scale changes.
- 32
- 32
- 31
- Automatic Micro-Expression Analysis: Open Challenges31
First completed system integrating micro-expression recognition and detection toward reading hidden emotions, which has outperformed the recognition capability of human subjects, is reported by MIT Technology Review (2015) and achieved increasing attention.
- 31
- LBP-TOP: A Tensor Unfolding Revisit31
The results show that the fast LBP-TOP implementation is much more time-saving than the original one, and the concept of tensor unfolding method to compute three concatenated big matrices in two-dimensional space is introduced.
- 31
- 30
- Scalable multi-label canonical correlation analysis for cross-modal retrieval30
A novel framework to simultaneously integrate the semantic correlation and feature correlation for cross-modal retrieval is proposed and it is shown that the model can avoid computing the covariance matrix explicitly explicitly which is a huge save of computational cost.
- 30
- 30
- Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks29
This work proposes a novel optimization framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem that can be solved using neural ODE and demonstrates that trajectory optimization and image reconstruction can be learned collaboratively for improved imaging efficiency and reconstruction performance.
- The 1st Challenge on Remote Physiological Signal Sensing (RePSS)29
An overview of the first challenge on Remote Physiological Signal Sensing (RePSS), in which two databases of VIPL and OBF are provided as the benchmark for kin researchers to evaluate their approaches, is presented.
- LOAD: Local orientation adaptive descriptor for texture and material classification29
The proposed LOAD enjoys not only discriminative power to capture the texture information, but also has strong robustness to illumination variation and image rotation, confirming that the LOAD is complementary to the learning-based features.
- 29
- Dense prediction for micro-expression spotting based on deep sequence model27
This paper proposes a novel method for ME spotting based on a deep sequence model that uses a LSTM network that can utilize both local and global correlation of the extracted feature to predict the score of the ME apex frame.
- 27
- Geometric Graph Representation With Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition26
This article investigates the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs with facial landmarks, only utilizing graph-based geometric features, and achieves competitive performance with a significantly reduced computational cost.
- Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition26
A weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch, and an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies to correct the ambiguous annotations.
- 26
- Toward Bridging Microexpressions From Different Domains26
This paper proposes an effective method consisting of an auxiliary set selection model (ASSM) and a transductive transfer regression model (TTRM) that has a more satisfactory performance in dealing with the cross-domain microexpression recognition tasks.
- OMEG: Oulu Multi-Pose Eye Gaze Dataset26
Large datasets with well-defined gaze directions are desired for pattern recognition tasks including eye gaze estimation because most researchers used normal face datasets, which are not specifically designed forEye gaze estimation.
- 25
- Sliding Window Based Micro-expression Spotting: A Benchmark25
A benchmark for fairer and better performance evaluation of micro-expression spotting approaches is constructed and a sliding window based multi-scale evaluation standard with a series of protocols is proposed.
- 24
- PFStorer: Personalized Face Restoration and Super-Resolution23
The robust capabilities of the approach, which is personalized using a few images of the identity, leading to tailored restoration with respect to the identity while retaining fine-grained de-tails, are showcased.
- Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features23
A general algorithm that achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms and is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.
- 22
- 22
- Exploring illumination robust descriptors for human epithelial type 2 cell classification22
A novel Spatial Shape Index Descriptor (SSID) is proposed to capture spatial layout information of the second-order structures, and a multi-scale Local Orientation Adaptive Descriptors (LOAD) is utilized to add to the robustness of the HEp-2 cell classification task.
- 22
- Texture Description with Completed Local Quantized Patterns22
The proposed completed local quantized patterns (CLQP) uses vector quantization to learn three separate codebooks for local sign, magnitude and orientation patterns, respectively and outperforms well-established features including LBP, LTP, CLBP,LQP on a range of challenging texture classification problems and an infant pain detection problem.
- 21
- MAC 2024: Micro-Action Analysis Grand Challenge21
This grand challenge aims to foster innovative research in micro-action analysis and provide benchmark evaluations to advance the technology in the human-centric action understanding community.
- 21
- Towards Reading Beyond Faces for Sparsity-aware 3D/4D Affect Recognition21
A sparsity-aware deep network is presented for automatic 4D facial expression recognition (FER) and an effective way to capture the facial muscle movements from three orthogonal plans (TOP), the TOP-landmarks over multi-views is introduced.
- Dynamic Facial Expression Recognition Using Boosted Component-Based Spatiotemporal Features and Multi-classifier Fusion21
Experimental studies conducted on the Cohn-Kanade database show that the approach that combines both boosted component-based spatiotemporal features and multi-classifier fusion strategy provides a better performance for expression recognition compared with earlier approaches.
- Data Leakage and Evaluation Issues in Micro-Expression Analysis20
It is found that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier.
- 20
- 20
- HEp-2 cell classification: The role of Gaussian Scale Space Theory as a pre-processing approach20
Under the BoW framework, the introduced pre-processing approach, using only one Local Orientation Adaptive Descriptor (LOAD), achieved superior performance on the Executable Thematic on Pattern Recognition Techniques for Indirect Immunofluorescence (ET-PRT-IIF) image analysis.
- Gesture interaction for wall-sized touchscreen display20
This research introduces gesture interaction into wall-sized touchscreen according to the distance between user and display, and creates two interaction modes for touch and body gesture respectively.
- Dynamic textures for human movement recognition20
This paper adopts the ideas of spatiotemporal analysis and the use of local features for motion description and uses Gentle AdaBoost to perform feature selection and build strong models from weak classifiers.
- Gait recognition using fractal scale20
Fractal scale wavelet analysis is applied to describe and automatically recognize gait, and by introducing the Mallat algorithm of wavelet, it reduces the computation complexity.
- MuSe 2021 Challenge19
The motivation, the sub-challenges, the challenge conditions, the participation, the Participation, and the most successful approaches are described.
- Saliency‐Based Segmentation of Optic Disc in Retinal Images19
Experimental results on two public databases for OD segmentation show that the proposed method achieves the state-of-the-art performance.
- 19
- A dual-branch neural network for DeepFake video detection by detecting spatial and temporal inconsistencies18
Experimental results show that the proposed dual-branch network model performs better than some recent spatial and temporal models for the Celeb-DF dataset and all the four manipulation methods in FaceForensics++ dataset since these two branches can complement each other.
- Geometry-Contrastive Transformer for Generalized 3D Pose Transfer18
It's demonstrated that the proposed novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes can achieve robust 3D pose transfer and be generalized to challenging meshes from unknown spaces on cross-dataset tasks.
- Expression Recognition in Videos Using a Weighted Component-Based Feature Descriptor18
Experimental results on the Extended Cohn-Kanade database show that the approach combining component-based spatiotemporal features descriptor and weight learning strategy achieves better recognition performance than the state of the art methods.
- Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset17
This article gathers interdisciplinary researchers to establish a multimodal dataset with cognitive and socio-emotional interactions for SSRL study among triadic group members, which is the first multimodal dataset for studying SSRL among triadic group members.
- 17
- 17
- 17
- 17
- 17
- Machine Learning for Human Motion Analysis: Theory and Practice17
This advanced publication addresses a broad audience including practicing professionals working with specific vision applications such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
- 16
- Disentangling 3D/4D Facial Affect Recognition With Faster Multi-View Transformer16
This paper proposes MiT: a novel multi-view transformer model for 3D/4D facial affect recognition that incorporates patch and position embeddings from various patches of multi-views and uses them for learning various facial muscle movements to showcase an effective recognition performance.
- A New Gabor Phase Difference Pattern for Face and Ear Recognition16
It is shown that the proposed method brings higher discriminative ability to Gabor phase based pattern, and it is observed that the method achieves the highest recognition rates among them.
- 16
- 16
- CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying15
A novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals, and generates high-fidelity rPPG features by matching them with noise-free PPG features from the codebook.
- Hybrid-Supervised Hypergraph-Enhanced Transformer for Micro-Gesture Based Emotion Recognition15
This work proposes to recognize the emotion states based on the micro-gestures by reconstructing behavioral patterns with a hypergraph-enhanced Transformer in a hybrid-supervised framework, and achieves the best performance under multiple metrics, which is superior to the existing methods.
- 15
- 15
- Saliency Integration: An Arbitrator Model15
This paper proposes an arbitrator model (AM) for saliency integration that incorporates the consensus of multiple saliency models and the external knowledge into a reference map to effectively rectify the misleading by candidate models and derives a Bayesian integration framework to reconcile theSaliency models of varying expertise and the reference map.
- Combining dynamic texture and structural features for speaker identification15
This paper proposes local spatiotemporal descriptors to represent and recognize speakers based solely on visual features, and results obtained promising recognition results comparing to the other features.
- Differentiable Auxiliary Learning for Sketch Re-Identification14
A differentiable auxiliary learning network (DALNet) is proposed to explore a robust auxiliary modality for Sketch Re-ID and the superior performance of the DALNet over the state-of-the-art methods for Sketch Re-ID, and the generalization in sketch-based image retrieval and sketch-photo face recognition tasks.
- 14
- 14
- RoLoD: Robust local descriptors for computer vision14
The aims are to highlight relevant work by researchers from related applications (such as one dimension (1D) digital signal processing, 2D images, 3D videos and 4D videos, and beyond), to provide a selection of papers that represent different perspectives and that will promote further research in the area.
- Texture Classification and Segmentation14
This chapter provides an introduction to the most common texture image test sets and overviews some texture classification experiments involving LBP descriptors and an unsupervised method for texture segmentation using LBP and contrast (LBP/C) distributions.
- 14
- 13
- 13
- 13
- Recent Progress on Face Presentation Attack Detection of 3D Mask Attacks13
This book chapter summarizes the progress in the past few years, as well as publicly available datasets, of face presentation attack detection (PAD) against 3D facial mask attack.
- 3D Skeletal Gesture Recognition via Sparse Coding of Time-Warping Invariant Riemannian Trajectories13
This paper represents each skeleton as a point in the product space of special orthogonal group SO3, which explicitly models the 3D geometric relationships between body parts, and presents a sparse coding of skeletal trajectories by explicitly considering the labeling information with each atoms to enforce the discriminant validity of dictionary.
- 13
- 12
- 12
- 12
- PCANet-II: When PCANet Meets the Second Order Pooling12
Compared with the histogram-based output, the second order pooling not only provides more discriminative information by preserving both the magnitude and sign of convolutional responses, but also dramatically reduces the size of output features.
- Emotion recognition from facial images with arbitrary views12
This paper proposes a new method to recognize arbitrary-view facial expressions by using discriminative neighborhood preserving embedding and multi-view concepts that first captures the discrim inative property of inter-class samples and explores the closeness of intra- class samples with arbitrary view in a low-dimensional subspace.
- Landmarks-assisted Collaborative Deep Framework for Automatic 4D Facial Expression Recognition11
Performance results obtained from extensive experimentation on the widely-adopted BU-4DFE database under globally used settings prove that the proposed collaborative framework outperforms the state-of-the-art 4D FER methods and reach a promising classification accuracy of 96.7% demonstrating its effectiveness.
- 11
- Sparse projections matrix binary descriptors for face recognition11
This paper introduces the sparsity constraint into the objective function of general binary codes learning framework, so that the problem of high computational cost and overfitting can be somehow solved and learns a discriminative and low-cost Sparse Projection Matrix Binary Descriptors (SPMBD).
- Violet-green excitation for NIR luminescence of Yb^3+ ions in Bi_2O_3-B_2O_3-SiO_2-Ga_2O_3 glasses11
It is proved that the violet-green excitations result in the NIR emission of Yb(3+), and an effective and controllable way is suggested to achieve nano-optical applications by Bi(0) metal nanoparticles/grains and Yb (3+).
- 11
- Improving Rotation Invariance of the Volume Local Binary Pattern Operator11
Improvements of the original VLBP operator are investigated, especially its robustness to rotation variations and effective compression of feature vectors, and methods for obtaining rotation invariance are experimentally evaluated.
- 11
- From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification10
The rationale of L2RW is that integrating decentralized training into VI-ReID can address privacy concerns in scenarios with limited data-sharing regulation, and it achieves performance comparable to SOTAs trained with shared data (privacy-unrestricted).
- 10
- 9
- 9
- 9
- 9
- 9
- 9
- 9
- Characterizing Subtle Facial Movements via Riemannian Manifold9
The tangent delta descriptor is proposed to characterize the dynamics of short-term movements using the differences between points on the tangent spaces to the manifolds, rather than the points themselves, to diminish the influences of extrinsic diversity.
- Bidirectional Long Short-Term Memory Variational Autoencoder.9
The proposed bLSTM-VAE, a novel variant of VAE whose encoders and decoders are implemented by bidirectional Long Short-Term Memory (bLSTM) networks, could learn latent variables reliably by mining the contextual information from the whole input sequence.
- 9
- 9
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 8
- 7
- MAC 2025: The 2nd Micro-Action Analysis Grand Challenge7
An overview of the 2nd Micro-Action Analysis Grand Challenge is presented, including its dataset, evaluation protocol, results, and discussion, and potential future directions are outlined to guide ongoing developments in this area.
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- SRN: Side-Output Residual Network for Object Reflection Symmetry Detection and Beyond7
Experimental results verify that the Sym-PASCAL benchmark is challenging related to real-world images, SRN achieves state-of-the-art performance, and MT-SRN has the capability to simultaneously predict edge and symmetry mask without loss of performance.
- 7
- 7
- 7
- 7
- 7
- 7
- 7
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- 6
- Saliency detection via bi-directional propagation6
The bi-directional propagation model (BIP) for saliency detection outperforms fourteen state-of-the-art saliency models on four challenging datasets, and largely enhances the propagation efficiency compared to single directional propagation models.
- 6
- 6
- 6
- 6
- FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition5
FVeinSyn, a synthetic generator capable of producing diverse finger vein patterns with rich intra-class variations, is introduced, creating FingerVeinSyn-5M, the largest available finger vein dataset -- containing 5 million samples from 50,000 unique fingers.
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- 5
- A Task-Driven Eye Tracking Dataset for Visual Attention Analysis5
Inspired by psychological findings that human visual behavior is tightly dependent on the executed tasks, this work carefully design specific tasks in accordance with the contents of 111 images covering various semantic categories, such as text, facial expression, texture, pose, and gaze.
- 5
- 5
- 5
- GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization4
This work proposes GaitProtector, an impersonation-driven gait deidentification framework that formulates privacy protection as a unified objective with two tightly coupled components: obfuscation and impersonation, which repels the protected gait from the source identity and attracts it toward a selected target identity.
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- 3
- Guest Editors’ Introduction to the Special Section on Compact and Efficient Feature Representation and Learning in Computer Vision3
The papers in this special section examine compact and efficient feature representation and learning in computer vision.
- 3
- 3
- 3
- 3
- 3
- An immersive fire training system using kinect3
Experimental results show the fire escape skills of 100 children are greatly improved and a simulation training system to help children learn fire hazards knowledge and escape skills is presented.
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- Salient Object Detection with CNNs and Multi-scale CRFs2
A more flexible CRF refinement framework by embedding the CRF inference to multiple levels of side outputs from CNNs for multi-scale saliency refinement and shows competitive performance over four datasets in comparison with the existing state-of-the-art saliency models.
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- Efficient Boosted Weak Classifiers for Object Detection1
Experimental results in the detection tasks for multiple classes of objects show that boosted PVCs significantly improves both learning and evaluation efficiency of nonlinear SVMs to the level of boosted linear classifiers, without losing any of the high discriminative power.
- 1
- 1
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- Machine learning in motion analysis: New advances–
The aims are to highlight relevant work by researchers from related disciplines (machine learning, computer vision, motion analysis, pattern recognition, biomedicine, and beyond), to provide a selection of papers that represents different perspectives and that will promote further research in the area.
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.