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Over the past few years, the importance of the facial micro-expression (FME) has garnered increasing attention from experts because of its potential applications from the judgment court to the psychology research centers. A real challenge for developing an extensive system of the FME analysis is to select a suitable method and a database. In this manuscript, we have conducted a comprehensive and comparative survey to address the aforementioned challenge, and to give clear guidelines to alleviate further researches. To come up with this task, we have justified each method in terms of its pros and cons, which are meant to be beneficial for researchers choosing a method or a database, which suits their context application. Also, we have exhaustively analyzed the whole framework of the FME system by decomposing its pipeline into the pre-processing, the feature extraction, and the classification. [ABSTRACT FROM AUTHOR]

Micro-expression is the involuntary emotion of the human that reflects the genuine feelings that cannot be hidden. Micro-expression is exhibited by facial expressions that last for a short duration and have very low intensity. Because of these reasons, micro-expression recognition is a challenging task. Recent research on the application of 3D convolutional neural networks (CNNs) has gained much popularity for video-based micro-expression analysis. For this purpose, both spatial as well as temporal features are of great importance to achieve high accuracies. The real possibly suppressed emotions of a person are valuable information for a variety of applications, such as in security, psychology, neuroscience, medicine and many other disciplines. This paper proposes a 3D CNN model architecture which is able to extract spatial and temporal features simultaneously. Thereby, the selection of the frame sequence plays a crucial role, since the emotions are only distinctive in a subset of the frames. Thus, we employ a novel pre-processing technique to select the Apex frame sequence from the entire video, where the timestamp of the most pronounced emotion is centered within this sequence. After an extensive evaluation including many experiments, the results show that the train–test split evaluation is biased toward a particular split and cannot be recommended in case of small and imbalanced datasets. Instead, a stratified K-fold evaluation technique is utilized to evaluate the model, which proves to be much more appropriate when using the three benchmark datasets CASME II, SMIC, and SAMM. Moreover, intra-dataset as well as cross-dataset evaluations were conducted in a total of eight different scenarios. For comparison purposes, two networks from the state of the art were reimplemented and compared with the presented architecture. In stratified K-fold evaluation, our proposed model outperforms both reimplemented state-of-the-art methods in seven out of eight evaluation scenarios. [ABSTRACT FROM AUTHOR]

Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide. Therefore, they can provide important information in a broad range of applications such as lie detection, criminal detection, etc. Since micro-expressions are transient and of low intensity, however, their detection and recognition is difficult and relies heavily on expert experiences. Due to its intrinsic particularity and complexity, video-based micro-expression analysis is attractive but challenging, and has recently become an active area of research. Although there have been numerous developments in this area, thus far there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences between macro- and micro-expressions, then use these differences to guide our research survey of video-based micro-expression analysis in a cascaded structure, encompassing the neuropsychological basis, datasets, features, spotting algorithms, recognition algorithms, applications and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are addressed and discussed. Furthermore, after considering the limitations of existing micro-expression datasets, we present and release a new dataset — called micro-and-macro expression warehouse (MMEW) — containing more video samples and more labeled emotion types. We then perform a unified comparison of representative methods on CAS(ME) $^2$ 2 for spotting, and on MMEW and SAMM for recognition, respectively. Finally, some potential future research directions are explored and outlined. [ABSTRACT FROM AUTHOR]

Understanding a person's attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance. [ABSTRACT FROM AUTHOR]

Micro-expression analysis is the study of subtle and fleeting facial expressions that convey genuine human emotions. Since such expressions cannot be controlled, many believe that it is an excellent way to reveal a human's inner thoughts. Analyzing micro-expressions manually is a very time-consuming and complicated task, hence many researchers have incorporated deep learning techniques to produce a more efficient analysis system. However, the insufficient amount of micro-expression data has limited the network's ability to be fully optimized, as overfitting is likely to occur if a deeper network is utilized. In this paper, a complete deep learning-based micro-expression analysis system is introduced that covers the two main components of a general automated system: spotting and recognition, with also an additional element of synthetic data augmentation. For the spotting part, an optimized continuous labeling scheme is introduced to spot the apex frame in a video. Once the apex frames have been recognized, they are passed to the generative adversarial network to produce an additional set of augmented apex frames. Meanwhile, for the recognition part, a novel convolutional neural network, coined as Optimal Compact Network (OC-Net), is introduced for the purpose of emotion recognition. The proposed system achieved the best F1-score of 0.69 in categorizing the emotions with the highest accuracy of 79.14%. In addition, the generated synthetic data used in the training phase also contributed to performance improvement of at least 0.61% for all tested networks. Therefore, the proposed optimized and compact deep learning system is suitable for mobile-based micro-expression analysis to detect the genuine human emotions. [ABSTRACT FROM AUTHOR]

Micro-expression is the involuntary emotion of the human that reflects the genuine feelings that cannot be hidden. Micro-expression is exhibited by facial expressions that last for a short duration and have very low intensity. Because of these reasons, micro-expression recognition is a challenging task. Recent research on the application of 3D convolutional neural networks (CNNs) has gained much popularity for video-based micro-expression analysis. For this purpose, both spatial as well as temporal features are of great importance to achieve high accuracies. The real possibly suppressed emotions of a person are valuable information for a variety of applications, such as in security, psychology, neuroscience, medicine and many other disciplines. This paper proposes a 3D CNN model architecture which is able to extract spatial and temporal features simultaneously. Thereby, the selection of the frame sequence plays a crucial role, since the emotions are only distinctive in a subset of the frames. Thus, we employ a novel pre-processing technique to select the Apex frame sequence from the entire video, where the timestamp of the most pronounced emotion is centered within this sequence. After an extensive evaluation including many experiments, the results show that the train–test split evaluation is biased toward a particular split and cannot be recommended in case of small and imbalanced datasets. Instead, a stratified K-fold evaluation technique is utilized to evaluate the model, which proves to be much more appropriate when using the three benchmark datasets CASME II, SMIC, and SAMM. Moreover, intra-dataset as well as cross-dataset evaluations were conducted in a total of eight different scenarios. For comparison purposes, two networks from the state of the art were reimplemented and compared with the presented architecture. In stratified K-fold evaluation, our proposed model outperforms both reimplemented state-of-the-art methods in seven out of eight evaluation scenarios.

Action Unit (AU) detection plays an important role in facial behaviour analysis. In the literature, AU detection has extensive researches in macro-expressions. However, to the best of our knowledge, there is limited research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Due to the small quantity and low intensity of micro-expression databases, micro-expression AU detection becomes challenging. To alleviate these problems, in this work, we propose 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. Through such spatial attention module, we expect to utilize rich relationship information of facial regions to increase the AU detection robustness on limited micro-expression samples. In addition, considering the low intensity of micro-expression AUs, we further propose to explore high-order statistics for better capturing subtle regional changes on face to obtain more discriminative AU features. Intensive experiments show that our proposed approach outperforms the basic framework by 0.0859 on CASME II, 0.0485 on CASME, and 0.0644 on SAMM in terms of the average F1-score. [ABSTRACT FROM AUTHOR]

Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.

Micro-expression analysis is the study of subtle and fleeting facial expressions that convey genuine human emotions. Since such expressions cannot be controlled, many believe that it is an excellent way to reveal a human’s inner thoughts. Analyzing micro-expressions manually is a very time-consuming and complicated task, hence many researchers have incorporated deep learning techniques to produce a more efficient analysis system. However, the insufficient amount of micro-expression data has limited the network’s ability to be fully optimized, as overfitting is likely to occur if a deeper network is utilized. In this paper, a complete deep learning-based micro-expression analysis system is introduced that covers the two main components of a general automated system: spotting and recognition, with also an additional element of synthetic data augmentation. For the spotting part, an optimized continuous labeling scheme is introduced to spot the apex frame in a video. Once the apex frames have been recognized, they are passed to the generative adversarial network to produce an additional set of augmented apex frames. Meanwhile, for the recognition part, a novel convolutional neural network, coined as Optimal Compact Network (OC-Net), is introduced for the purpose of emotion recognition. The proposed system achieved the best F1-score of 0.69 in categorizing the emotions with the highest accuracy of 79.14%. In addition, the generated synthetic data used in the training phase also contributed to performance improvement of at least 0.61% for all tested networks. Therefore, the proposed optimized and compact deep learning system is suitable for mobile-based micro-expression analysis to detect the genuine human emotions.