Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition

Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to effectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance.

Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition

Micro-expression recognition (MER) is valuable because the involuntary nature of micro-expressions (MEs) can reveal genuine emotions. Most works recognize MEs by taking RGB videos or images as input. In fact, the activated facial regions in ME images are very small and the subtle motion can be easily submerged in the unrelated information. Facial landmarks are a low-dimensional and compact modality, which leads to much lower computational cost and can potentially concentrate more on ME-related features. However, the discriminability of landmarks for MER is not clear. Thus, this paper explores the contribution of facial landmarks and constructs a new framework to efficiently recognize MEs with sole facial landmark information. Specially, we design a separate structure module to separately aggregate the spatial and temporal information in the geometric movement graph based on facial landmarks, and a Geometric Two-Stream Graph Network is constructed to aggregate the low-order geometric information and high-order semantic information of facial landmarks. Furthermore, two core components are proposed to enhance features. Specifically, a semantic adjacency matrix can automatically model the relationship between nodes even long-distance nodes in a self-learning fashion; and an Adaptive Action Unit loss is introduced to guide the learning process such that the learned features are forced to have a synchronized pattern with facial action units. Notably, this work tackles MER only utilizing geometric features, processed based on a graph model, which provides a new idea with much higher efficiency to promote MER. The experimental results demonstrate that the proposed method can achieve competitive or even superior performance with a significantly reduced computational cost, and facial landmarks can significantly contribute to MER and are worth further study for efficient ME analysis.

A comparative study on movement feature in different directions for micro-expression recognition

Micro-expression can reflect people's real emotions. Recognizing micro-expressions is difficult because they are small motions and have a short duration. As the research is deepening into micro-expression recognition, many effective features and methods have been proposed. To determine which direction of movement feature is easier for distinguishing micro-expressions, this paper selects 18 directions (including three types of horizontal, vertical and oblique movements) and proposes a new low-dimensional feature called the Histogram of Single Direction Gradient (HSDG) to study this topic. In this paper, HSDG in every direction is concatenated with LBP-TOP to obtain the LBP with Single Direction Gradient (LBP-SDG) and analyze which direction of movement feature is more discriminative for micro-expression recognition. As with some existing work, Euler Video Magnification (EVM) is employed as a preprocessing step. The experiments on the CASME II and SMIC-HS databases summarize the effective and optimal directions and demonstrate that HSDG in an optimal direction is discriminative, and the corresponding LBP-SDG achieves state-of-the-art performance using EVM.