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

Spatio-temporal convolutional emotional attention network for spotting macro- and micro-expression intervals in long video sequences

PERSIST: Improving micro-expression spotting using better feature encodings and multi-scale Gaussian TCN

Recently, micro-expression detection has attracted much attention, since micro-expression has potential lie detection applications. In this paper, a micro-expression detection algorithm based on action units (AUs) and multi-region feature fusion is proposed. First, the regions of interest (ROIs) are selected based on AUs. Second, the optical flow features in spatio-temporal domain and frequency domain features are fused as a final feature descriptions of micro-expressions. Finally, a decision criterion based on the characteristic of the optical flow angle, magnitude and frequency magnitude is applied to spot onset, apex, and offset frames from micro-expression video sequences. The experiments are performed on SDU_spotting and CASME II datasets. The results show that the proposed method obtains higher performance than the state-of-the-art methods in terms of accuracy.

Micro-expression Detection based on Action Units and Multi-region Feature Fusion

Micro-expression recognition with attention mechanism and region enhancement

Microexpression (ME) spotting is a crucial step for emotion analysis to detect people’s true emotions. However, the short duration, small motion amplitude, and limited sample number make accurate spotting and ME-locating challenging. To address these problems, we make two contributions in this article, including an ME dataset SDU2 and a spotting method. The dataset SDU2 contains hybrid expressions of 1602 video clips labeled by professional psychologists, covering six main categories of emotions with balanced distribution. Our ME spotting method is based on a magnitude- and angle-combined optical flow feature, exploiting the angle information, which has been overlooked by other spotting methods. In this method, we select four most discriminative regions of interest (ROIs) and determine the appearance of MEs using the proposed feature and a novel decision criterion based on the characteristic of the optical flow angle and magnitude. We have conducted experiments on SDU2 and CASME II datasets. The results demonstrate that our method achieves much better performance compared to other state-of-the-art methods in terms of spotting accuracy.

A Magnitude and Angle Combined Optical Flow Feature for Microexpression Spotting

Different from the traditional macro-expressions, micro-expressions are unconscious, quick and trustworthy facial expressions, which can reveal real emotion. Micro-expressions can provide information that is important and crucial in applications such as lie detection, criminal investigation, pain or mood assessment, etc. However, it is worth noting that most current micro-expression recognition methods rely only on a single micro-expression database. If the training and test samples belong to different domains, for example, different micro-expression databases, the accuracy of existing micro-expression recognition methods will decrease dramatically. To solve this problem, we propose an unsupervised cross-database micro-expression recognition method based on distribution adaptation. Compared with most advanced unsupervised cross-database recognition methods, the proposed method has better performance on micro-expression recognition tasks. 

Bing Li

Papers

A Magnitude and Angle Combined Optical Flow Feature for Microexpression Spotting

Unsupervised cross-database micro-expression recognition based on distribution adaptation

Micro-expression Detection based on Action Units and Multi-region Feature Fusion