Spontaneous Micro-expression Research Articles

Cross-dataset micro-expression identification based on facial ROIs contribution quantification

Abstract In this work, we propose a novel cross-dataset micro-expression identification method based on facial regions of interest contribution quantification, where the training samples are from the source dataset and the test samples are from the target dataset. Specifically, a micro-expression video clip (i.e., a sample) is first sampled at intervals to capture an image sequence consisting of multiple consecutive video frames. Then, locate and crop the facial area in the image frame, and perform facial landmark detection on the cropped area. Next calculate the optical-flow field of the image sequence, and extract the improved Main Directional Mean Optical-flow feature. Finally, the learned group sparse model is used as the classifier to predict the label information of the unmarked target samples, so as to complete the category recognition of micro-expressions. Extensive cross-dataset comparative experiments on four spontaneous micro-expression datasets, CASME, CASME II, SMIC-HS, and SAMM, show that the recognition strategy proposed in this work is effective. Compared with several other advanced recognition methods, our method has better cross-dataset recognition performance, not only the classification accuracy is higher, but also the classification stability is better when faced with different target datasets and different micro-expression categories. It can be applied in many fields such as clinical diagnosis, interrogation, negotiation, and national security.

Micro-expression recognition based on euler video magnification and 3D residual network under imbalanced sample

A student’s verbal behavior plays a crucial role in education, while nonverbal behavior, such as micro-expressions, significantly improves teaching quality. To address the problem of small facial expression movements, imbalanced data categories, and lack of temporal information in static expressions, a micro-expression recognition method is proposed based on Eulerian Video Magnification (EVM) and a 3D Residual Network (3D ResNet) under imbalanced samples. Firstly, face detection in the Dlib library is used to locate the face in the micro-expression video sample and crop it. Secondly, the EVM is used to magnify the motion features in micro-expressions. Then, the 3D ResNet is used to extract spatio-temporal information from micro-expression video samples, and the Cyclical Focal Loss (CFL) function is introduced in the network training process to solve the class imbalance problem in micro-expression datasets. Finally, the roles of the EVM and the CFL function in recognizing micro-expressions by the 3D ResNet are analyzed. The experimental results on the Spontaneous Micro-expression Database (SMIC) and Chinese Academy of Sciences Micro-expression Database II (CASME II) demonstrate the effectiveness and superiority of this method. The proposed method can assist in teaching evaluation and promote the development of smart classrooms, and further research is needed on the storage and computing of the proposed method on devices.

Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition.

Micro-expressions, which are spontaneous and difficult to suppress, reveal a person's true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion computing. In recent years, deep learning-based feature extraction and fusion techniques have been widely used for micro-expression recognition, particularly methods based on Vision Transformer that have gained popularity. However, the Vision Transformer-based architecture used in micro-expression recognition involves a significant amount of invalid computation. Additionally, in the traditional two-stream architecture, although separate streams are combined through late fusion, only the output features from the deepest level of the network are utilized for classification, thus limiting the network's ability to capture subtle details due to the lack of fine-grained information. To address these issues, we propose a new two-level spatio-temporal feature fused with a two-stream architecture. This architecture includes a spatial encoder (modified ResNet) for learning texture features of the face, a temporal encoder (Swin Transformer) for learning facial muscle motor features, a feature fusion algorithm for integrating multi-level spatio-temporal features, a classification head, and a weighted average operator for temporal aggregation. The two-stream architecture has the advantage of extracting richer features compared to the single-stream architecture, leading to improved performance. The shifted window scheme of Swin Transformer restricts self-attention computation to non-overlapping local windows and allows cross-window connections, significantly improving the performance and reducing the computation compared to Vision Transformer. Moreover, the modified ResNet is computationally less intensive. Our proposed feature fusion algorithm leverages the similarity in output feature shapes at each stage of the two streams, enabling the effective fusion of multi-level spatio-temporal features. This algorithm results in an improvement of approximately 4% in both the F1 score and the UAR. Comprehensive evaluations conducted on three widely used spontaneous micro-expression datasets (SMIC-HS, CASME II, and SAMM) consistently demonstrate the superiority of our approach over comparative methods. Notably, our approach achieves a UAR exceeding 0.905 on CASME II, making it one of the few frameworks in the published micro-expression recognition literature to achieve such high performance.

Improving the Accuracy of Facial Micro-Expression Recognition: Spatio-Temporal Deep Learning with Enhanced Data Augmentation and Class Balancing

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

4DME: A Spontaneous 4D Micro-Expression Dataset With Multimodalities

Micro-expressions (ME) are a special form of facial expressions which may occur when people try to hide their true feelings for some reasons. MEs are important clues to reveal people’s true feelings, but are difficult or impossible to be captured by ordinary persons with naked-eyes as they are very short and subtle. It is expected that robust computer vision methods can be developed to automatically analyze MEs which requires lots of ME data. The current ME datasets are insufficient, and mostly contain only one single form of 2D color videos. Researches on 4D data of ordinary facial expressions have prospered, but so far no 4D data is available in ME study. In the current study, we introduce the 4DME dataset: a new spontaneous ME dataset which includes 4D data along with three other video modalities. Both micro- and macro-expression clips are labeled out in 4DME, and 22 AU labels and five categories of emotion labels are annotated. Experiments are carried out using three 2D-based methods and one 4D-based method to provide baseline results. The results indicate that the 4D data can potentially benefit ME recognition. The 4DME dataset could be used for developing 4D-based approaches, or exploring fusion of multiple video sources (e.g., texture and depth) for the task of ME analysis in future. Besides, we also emphasize the importance of forming a clear and unified criteria of ME annotation for future ME data collection studies. Several key questions related with ME annotation are listed and discussed in depth, especially about the relationship between AUs and ME emotion categories. A preliminary AU-Emo mapping table is proposed with justified explanations and supportive experimental results. Several unsolved issues are also summarized for future work.

Highly effective end-to-end single-to-multichannel feature fusion and ensemble classification to decode emotional secretes from small-scale spontaneous facial micro-expressions

Facial Micro-Expression (ME) is one of the pre-dominating non-verbal clues to demystify the true emotional states that people try to conceal cautiously. But emotion recognition from spontaneous ME images limits the high accuracy due to short duration and low intensity, lack of sufficient samples and consistencies among publicly available ME datasets. In this study, we have proposed two highly effective, lightweight, and generalized single-channel DLRRF-MER, and multi-channel DLH-3C-FUSION fusion models inspired by deep dense convolutional models and texture-based feature descriptors Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to recognize ME from apex frame by constructing a composite dataset from five publicly available ME datasets CASME, CASMEII, CAS(ME)2, SAMM and MMEW. Pre-training has been done on a new composition of five facial macro expressions datasets CK+, MUGFE, OuluCasia, SFEW, and RAF-DB. The proposed models are fine-tuned on the target ME dataset rigorously with Stratified 5-Fold and 10-Fold, Leave-One-Subject-Out, and Leave-One-Dataset-Out(LODO) cross-validations(CV). In all evaluations, our proposed algorithms show remarkable improvement in effectiveness which surpasses the state-of-the-art accuracies and results in higher generalization capacity.

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition

Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.

Deep3DCANN: A Deep 3DCNN-ANN framework for spontaneous micro-expression recognition

Facial micro-expressions play a significant role in revealing concealed emotions. However, the recognition of micro-expressions is challenging due to their fleeting nature. Moreover, the visual features of the face and the visual relationships between the facial sub-regions have a strong influence on the presence of micro-expressions. In this work, a novel end-to-end facial micro-expression detection framework, called Deep3DCANN, is proposed to integrate these components for effective micro-expression detection. The first component of our framework is a deep 3D convolutional neural network that learns useful spatiotemporal features from a sequence of facial images. In the second component, a deep artificial neural network is utilized to trace the useful visual associations between different sub-regions of the face. Furthermore, a carefully crafted fusion mechanism is built to combine the learned facial features and the semantic relationships between the regions to predict the micro-expressions. We also construct a new loss function to jointly optimize both modules of our proposed architecture. Our proposed method performs favourably on five benchmark spontaneous micro-expression databases compared to existing micro-expression recognition baselines on videos. In addition, through an extended experiment, we show that our proposed approach can effectively recognize the frame-wise micro-expression changes in a sequence of video frames.

Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition

Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensity, the recognition of micro-expressions is a difficult task in affective computing. The early work based on handcrafted spatio-temporal features which showed some promise, has recently been superseded by different deep learning approaches which now compete for the state of the art performance. Nevertheless, the problem of capturing both local and global spatio-temporal patterns remains challenging. To this end, herein we propose a novel spatio-temporal transformer architecture -- to the best of our knowledge, the first purely transformer based approach (i.e. void of any convolutional network use) for micro-expression recognition. The architecture comprises a spatial encoder which learns spatial patterns, a temporal aggregator for temporal dimension analysis, and a classification head. A comprehensive evaluation on three widely used spontaneous micro-expression data sets, namely SMIC-HS, CASME II and SAMM, 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 on any of the aforementioned data sets.

Transcranial Direct Current Stimulation Over the Right Temporal Parietal Junction Facilitates Spontaneous Micro-Expression Recognition.

Micro-expressions are fleeting and subtle emotional expressions. As they are spontaneous and uncontrollable by one’s mind, micro-expressions are considered an indicator of genuine emotions. Their accurate recognition and interpretation promote interpersonal interaction and social communication. Therefore, enhancing the ability to recognize micro-expressions has captured much attention. In the current study, we investigated the effects of training on micro-expression recognition with a Chinese version of the Micro-Expression Training Tool (METT). Our goal was to confirm whether the recognition accuracy of spontaneous micro-expressions could be improved through training and brain stimulation. Since the right temporal parietal junction (rTPJ) has been shown to be involved in the explicit process of facial emotion recognition, we hypothesized that the rTPJ would play a role in facilitating the recognition of micro-expressions. The results showed that anodal transcranial direct-current stimulation (tDCS) of the rTPJ indeed improved the recognition of spontaneous micro-expressions, especially for those associated with fear. The improved accuracy of recognizing fear spontaneous micro-expressions was positively correlated with personal distress in the anodal group but not in the sham group. Our study supports that the combined use of tDCS and METT can be a viable way to train and enhance micro-expression recognition.

Comparative analysis of super-resolution reconstructed images for micro-expression recognition

It is an established fact that the genuineness of facial micro-expression is an effective means for estimating concealed emotions (Li et al. in Micro-expression recognition under low-resolution cases. SciTePress, Science and Technology Publications, Setúbal, 2019). Conventionally, analysis of these expressions has been performed using high resolution images which are ideal cases. However, in a real-world scenario, capturing expressions with high resolution images may not always be possible particularly using low-cost surveillance cameras. Faces captured using such cameras are often very tiny and of poor resolution. Due to the loss of discriminative features these images may not be of much use particularly for identifying certain minute facial details. To make these images useful, enhancing the textural information becomes essential and super-resolution algorithms can be ideal to achieve this. In this work, we utilize algorithms based on deep learning and generative adversarial network for transforming low-resolution micro-expression images into super-resolution images and examine their fitness particularly for micro-expression recognition. The proposed approach is tested on simulated dataset obtained from two popular spontaneous micro-expression datasets namely CASME II and SMIC-VIS; the experimental results demonstrate that the method achieved favourable results with the best recognition performance recorded as 61.63%. The significance of this work is: first, it thoroughly investigates reconstruction performance of several deep learning super-resolution algorithms on simulated low-quality micro-expression images; second, it provides a comprehensive analysis of the results obtained employing these reconstructed images to determine their contribution in addressing image quality issues specifically for micro-expression recognition.

A novel micro-expression detection algorithm based on BERT and 3DCNN

As a special type of facial expressions, the spontaneous micro-expressions can reveal the genuine emotions that people attempt to hide, therefore can provide potential information in criminal detection, lie detection, etc. Compared to ordinary facial expressions, micro-expressions are involuntary, transient and of low intensity. Consequently, micro-expression detection is difficult and overly dependent on expert experiences. Thus, we propose a novel micro-expression detection method based on the Bidirectional Encoder Representation from Transformers (BERT) network, namely R3D_BERT+Group, which includes the candidate segment generation module, spatio-temporal feature extraction module and grouping module. Specifically, the candidate segments are generated by the candidate segment generation module, then each candidate segment is divided into smaller time slots by the spatio-temporal feature extraction module, where spatio-temporal features are extracted through 3DCNN and BERT network. Finally, consecutive segments are merged and overlapping segments are suppressed by the grouping module to locate the position of the onset and offset frames of the micro-expression more accurately. Comprehensive experiments on CASME 2 and SDU_spotting databases firmly demonstrate the effectiveness of our method over other state-of-the-art detection methods.

CAS(ME)3: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity.

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) 3. The contribution of this article is summarized as follows: (1) CAS(ME) 3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) 3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) 3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) 3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

Apex Frame Spotting Using Attention Networks for Micro-Expression Recognition System

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) andChinese 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.

Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network.

Microexpression can manifest the real mood of humans, which has been widely concerned in clinical diagnosis and depression analysis. To solve the problem of missing discriminative spatiotemporal features in a small data set caused by the short duration and subtle movement changes of microexpression, we present a dual-stream spatiotemporal attention network (DSTAN) that integrates dual-stream spatiotemporal network and attention mechanism to capture the deformation features and spatiotemporal features of microexpression in the case of small samples. The Spatiotemporal networks in DSTAN are based on two lightweight networks, namely, the spatiotemporal appearance network (STAN) learning the appearance features from the microexpression sequences and the spatiotemporal motion network (STMN) learning the motion features from optical flow sequences. To focus on the discriminative motion areas of microexpression, we construct a novel attention mechanism for the spatial model of STAN and STMN, including a multiscale kernel spatial attention mechanism and global dual-pool channel attention mechanism. To obtain the importance of each frame in the microexpression sequence, we design a temporal attention mechanism for the temporal model of STAN and STMN to form spatiotemporal appearance network-attention (STAN-A) and spatiotemporal motion network-attention (STMN-A), which can adaptively perform dynamic feature refinement. Finally, the feature concatenate-SVM method is used to integrate STAN-A and STMN-A to a novel network, DSTAN. The extensive experiments on three small spontaneous microexpression data sets of SMIC, CASME, and CASME II demonstrate the proposed DSTAN can effectively cope with the recognition of microexpressions.

Micro-Expression Recognition Base on Optical Flow Features and Improved MobileNetV2

When a person tries to conceal emotions, real emotions will manifest themselves in the form of micro-expressions. Research on facial micro-expression recognition is still extremely challenging in the field of pattern recognition. This is because it is difficult to implement the best feature extraction method to cope with micro-expressions with small changes and short duration. Most methods are based on hand-crafted features to extract subtle facial movements. In this study, we introduce a method that incorporates optical flow and deep learning. First, we take out the onset frame and the apex frame from each video sequence. Then, the motion features between these two frames are extracted using the optical flow method. Finally, the features are inputted into an improved MobileNetV2 model, where SVM is applied to classify expressions. In order to evaluate the effectiveness of the method, we conduct experiments on the public spontaneous micro-expression database CASME II. Under the condition of applying the leave-one-subject-out cross-validation method, the recognition accuracy rate reaches 53.01%, and the F-score reaches 0.5231. The results show that the proposed method can significantly improve the micro-expression recognition performance.

Single trunk multi-scale network for micro-expression recognition

Micro-expressions are the external manifestations of human psychological activities. Therefore, micro-expression recognition has important research and application value in many fields such as public services, criminal investigations, and clinical diagnosis. However, the particular characteristics (e.g., short duration and subtle changes) of micro-expressions bring great challenges to micro-expression recognition. In this paper, we explore the differences in the direction of facial muscle movement when people make different expressions to recognize micro-expressions. We first use optical flow to capture the subtle changes in the facial movement when a micro-expression occurs. Next, we extract facial movement information to an aniso-weighted optical flow image based on anisotropically weighting the horizontal and vertical components of the optical flow. Finally, we feed the aniso-weighted optical flow image into the proposed Single Trunk Multi-scale Network for micro-expression recognition. In particular, the designed multi-scale feature catcher in the network can capture features of micro-expressions with different intensities. We conduct extensive experiments on four spontaneous micro-expression datasets, and the experiment results show that our proposed method is competitive and effective.

A two-stage 3D CNN based learning method for spontaneous micro-expression recognition

Micro-expressions (MEs) are spontaneous and involuntary facial subtle reactions which often reveal the genuine emotions within human beings. Recognizing MEs automatically is becoming increasingly crucial for many areas such as diagnosis and security. However, the short time duration and low spatial intensity of MEs pose great challenges for accurately recognizing them. Additionally, the lack of sufficient and balanced spontaneous MEs data also makes this problem even harder to solve, and some adaptive modeling strategies have been quite urgent recently. To this end, this paper draws inspirations from few-shot learning to propose a novel two-stage learning (i.e., prior learning and target learning) method based on a siamese 3D convolutional neural network for MEs recognition (MERSiamC3D). Specifically, in the prior learning stage, the proposed MERSiamC3D is used to extract the generic features of MEs. In the target learning stage, the structure and parameters of the MERSiamC3D will be carefully adjusted and the Focal Loss is adopted for high-level features learning. Afterwards, in order to effectively retain the spatiotemporal information of the original MEs video, an adaptive construction method based on adaptive convolutional neural network is proposed to construct the key-frames sequence to summarize the original MEs video, which is able to help drop the redundant frames and relatively highlight the movement of the apex frame. Then, the new key-frames are taken as the input of the two-stage learning method. Finally, through extensive evaluations and experiments on three publically available MEs datasets, the proposed method in this work could outperform traditional methods and other deep learning baselines, which provides a novel insight on how to leverage scarce data for MEs recognition.

MESNet: A Convolutional Neural Network for Spotting Multi-Scale Micro-Expression Intervals in Long Videos.

Micro-expression spotting is a fundamental step in the micro-expression analysis. This paper proposes a novel network based convolutional neural network (CNN) for spotting multi-scale spontaneous micro-expression intervals in long videos. We named the network as Micro-Expression Spotting Network (MESNet). It is composed of three modules. The first module is a 2+1D Spatiotemporal Convolutional Network, which uses 2D convolution to extract spatial features and 1D convolution to extract temporal features. The second module is a Clip Proposal Network, which gives some proposed micro-expression clips. The last module is a Classification Regression Network, which classifies the proposed clips to micro-expression or not, and further regresses their temporal boundaries. We also propose a novel evaluation metric for spotting micro-expression. Extensive experiments have been conducted on the two long video datasets: CAS(ME)2 and SAMM, and the leave-one-subject-out cross-validation is used to evaluate the spotting performance. Results show that the proposed MESNet effectively enhances the F1-score metric. And comparative results show the proposed MESNet has achieved a good performance, which outperforms other state-of-the-art methods, especially in the SAMM dataset.

Sparse Spatiotemporal Descriptor for Micro-Expression Recognition Using Enhanced Local Cube Binary Pattern

As a spontaneous facial expression, a micro-expression can reveal the psychological responses of human beings. Thus, micro-expression recognition can be widely studied and applied for its potentiality in clinical diagnosis, psychological research, and security. However, micro-expression recognition is a formidable challenge due to the short-lived time frame and low-intensity of the facial actions. In this paper, a sparse spatiotemporal descriptor for micro-expression recognition is developed by using the Enhanced Local Cube Binary Pattern (Enhanced LCBP). The proposed Enhanced LCBP is composed of three complementary binary features containing Spatial Difference Local Cube Binary Patterns (Spatial Difference LCBP), Temporal Direction Local Cube Binary Patterns (Temporal Direction LCBP), and Temporal Gradient Local Cube Binary Patterns (Temporal Gradient LCBP). With the application of Enhanced LCBP, it would no longer be a problem to provide binary features with spatiotemporal domain complementarity to capture subtle facial changes. In addition, due to the redundant information existing among the division grids, which affects the ability of descriptors to distinguish micro-expressions, the Multi-Regional Joint Sparse Learning is designed to perform feature selection for the division grids, thus paying more attention to the critical local regions. Finally, the Multi-kernel Support Vector Machine (SVM) is employed to fuse the selected features for the final classification. The proposed method exhibits great advantage and achieves promising results on four spontaneous micro-expression datasets. Through further observation of parameter evaluation and confusion matrix, the sufficiency and effectiveness of the proposed method are proved.