Automatic Facial Action Unit Research Articles

Hard semantic mask strategy for automatic facial action unit recognition with teacher–student model

Hard semantic mask strategy for automatic facial action unit recognition with teacher–student model

MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Unit Detection

The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images, which has attracted extensive research attention due to its wide use in facial expression analysis. Many methods that perform well on automatic facial action unit (AU) detection primarily focus on modeling various AU relations between corresponding local muscle areas or mining global attention–aware facial features; however, they neglect the dynamic interactions among local-global features. We argue that encoding AU features just from one perspective may not capture the rich contextual information between regional and global face features, as well as the detailed variability across AUs, because of the diversity in expression and individual characteristics. In this article, we propose a novel Multi-level Graph Relational Reasoning Network (termed MGRR-Net ) for facial AU detection. Each layer of MGRR-Net performs a multi-level (i.e., region-level, pixel-wise, and channel-wise level) feature learning. On the one hand, the region-level feature learning from the local face patch features via graph neural network can encode the correlation across different AUs. On the other hand, pixel-wise and channel-wise feature learning via graph attention networks (GAT) enhance the discrimination ability of AU features by adaptively recalibrating feature responses of pixels and channels from global face features. The hierarchical fusion strategy combines features from the three levels with gated fusion cells to improve AU discriminative ability. Extensive experiments on DISFA and BP4D AU datasets show that the proposed approach achieves superior performance than the state-of-the-art methods.

Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various weakly supervised methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network. Furthermore, by incorporating semi-supervised learning, we propose an end-to-end trainable framework named weakly supervised regional and temporal learning (WSRTL) for AU recognition. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.

GeoConv: Geodesic guided convolution for facial action unit recognition

Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture. Most existing AU recognition approaches leverage geometry information in a straightforward 2D or 3D manner, which either ignore 3D manifold information or suffer from high computational costs. In this paper, we propose a novel geodesic guided convolution (GeoConv) for AU recognition by embedding 3D manifold information into 2D convolutions. Specifically, the kernel of GeoConv is weighted by our introduced geodesic weights, which are negatively correlated to geodesic distances on a coarsely reconstructed 3D morphable face model. Moreover, based on GeoConv, we further develop an end-to-end trainable framework named GeoCNN for AU recognition. Extensive experiments on BP4D and DISFA benchmarks show that our approach significantly outperforms the state-of-the-art AU recognition methods.

A comprehensive survey on automatic facial action unit analysis

Facial Action Coding System is the most influential sign judgment method for facial behavior, and it is a comprehensive and anatomical system which could encode various facial movements by the combination of basic AUs (Action Units). AUs define certain facial configurations caused by contraction of one or more facial muscles, and they are independent of interpretation of emotions. However, automatic facial action unit recognition remains challenging due to several open questions such as rigid and non-rigid facial motions, multiple AUs detection, intensity estimation and naturalistic context application. This paper introduces recent advances in automatic facial action unit recognition, focusing on the fundamental components of face detection and registration, facial action representation, feature selection and classification. The comprehensive analysis of facial representations is presented according to the facial data properties (image and video, 2D and 3D) and characteristics of facial features (predesign and learning, appearance and geometry, hybrid and fusion). Facial action unit recognition involves AUs occurrence detection, AUs temporal segment detection and AUs intensity estimation. We discussed the role of each component, main techniques with their characteristics, challenges and potential directions of facial action unit analysis.

Cascade of Tasks for facial expression analysis

Automatic facial action unit (AU) detection from video is a long-standing problem in facial expression analysis. Existing work typically poses AU detection as a classification problem between frames or segments of positive and negative examples, and emphasizes the use of different features or classifiers. In this paper, we propose a novel AU event detection method, Cascade of Tasks (CoT), which combines the use of different tasks (i.e., frame-level detection, segment-level detection and transition detection). We train CoT sequentially embracing diversity to ensure robustness and generalization to unseen data. Unlike conventional frame-based metrics that evaluate frames independently, we propose a new event-based metric to evaluate detection performance at the event-level. The event-based metric measures the ratio of correctly detected AU events instead of frames. We show how the CoT method consistently outperforms state-of-the-art approaches in both frame-based and event-based metrics, across four datasets that differ in complexity: CK+, FERA, RU-FACS and GFT.

Selective Transfer Machine for Personalized Facial Expression Analysis.

Automatic facial action unit (AU) and expression detection from videos is a long-standing problem. The problem is challenging in part because classifiers must generalize to previously unknown subjects that differ markedly in behavior and facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) from those on which the classifiers are trained. While some progress has been achieved through improvements in choices of features and classifiers, the challenge occasioned by individual differences among people remains. Person-specific classifiers would be a possible solution but for a paucity of training data. Sufficient training data for person-specific classifiers typically is unavailable. This paper addresses the problem of how to personalize a generic classifier without additional labels from the test subject. We propose a transductive learning method, which we refer to as a Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific mismatches. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. We compared STM to both generic classifiers and cross-domain learning methods on four benchmarks: CK+ [44], GEMEP-FERA [67], RUFACS [4] and GFT [57]. STM outperformed generic classifiers in all.

Facial Action Unit Event Detection by Cascade of Tasks.

Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and negative ones, where existing work emphasizes the use of different features or classifiers. In this paper, we propose a method called Cascade of Tasks (CoT) that combines the use of different tasks (i.e., frame, segment and transition) for AU event detection. We train CoT in a sequential manner embracing diversity, which ensures robustness and generalization to unseen data. In addition to conventional frame-based metrics that evaluate frames independently, we propose a new event-based metric to evaluate detection performance at event-level. We show how the CoT method consistently outperforms state-of-the-art approaches in both frame-based and event-based metrics, across three public datasets that differ in complexity: CK+, FERA and RU-FACS.

Selective Transfer Machine for Personalized Facial Action Unit Detection.

Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all.

Dynamic Cascades with Bidirectional Bootstrapping for Action Unit Detection in Spontaneous Facial Behavior.

Automatic facial action unit detection from video is a long-standing problem in facial expression analysis. Research has focused on registration, choice of features, and classifiers. A relatively neglected problem is the choice of training images. Nearly all previous work uses one or the other of two standard approaches. One approach assigns peak frames to the positive class and frames associated with other actions to the negative class. This approach maximizes differences between positive and negative classes, but results in a large imbalance between them, especially for infrequent AUs. The other approach reduces imbalance in class membership by including all target frames from onsets to offsets in the positive class. However, because frames near onsets and offsets often differ little from those that precede them, this approach can dramatically increase false positives. We propose a novel alternative, dynamic cascades with bidirectional bootstrapping (DCBB), to select training samples. Using an iterative approach, DCBB optimally selects positive and negative samples in the training data. Using Cascade Adaboost as basic classifier, DCBB exploits the advantages of feature selection, efficiency, and robustness of Cascade Adaboost. To provide a real-world test, we used the RU-FACS (a.k.a. M3) database of nonposed behavior recorded during interviews. For most tested action units, DCBB improved AU detection relative to alternative approaches.