Facial Micro-Expression Recognition is a challenging task in identifying suppressed emotion in a high-stake environment, often comes in very brief duration and subtle changes.
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.
It aims to obtain salient and discriminative features for specific expressions and also predict expression by fusing the expression-specific features.
In this paper, we propose a novel deep neural network model for objective class-based MER, which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks (GCN).
We propose a facial micro-expression recognition model using 3D residual attention network called MERANet.
On the other hand, some methods based on deep learning also cannot get high accuracy due to problems such as the imbalance of databases.
Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER).
In this paper, we analyze the influence of learning complexity, including the input complexity and model complexity, and discover that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task.
However, existing networks fail to establish a relationship between spatial features of facial appearance and temporal variations of facial dynamics.
Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second.