# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
## 2 Related Works  
Initial efforts [8,9] made noteworthy advancements in the main mechanism of the ME-detecting task. Specifically, a 2-distance feature difference analysis is employed between two frames within a preset time interval, using a local binary pattern (LBP) as the feature descriptor. The ME is detected if the frame's feature vector exceeds the peak detection threshold. Similarly, using a block-by-block face division technique, [PERSON] et al. [10] calculated each frame's Histogram of Oriented Gradient (HOG). Then, this method detects MEs by calculating the difference between frames at a predetermined interval using the Chi-Squared distance. These are the traditional hand-crafted methods that contributed to breakthroughs in ME detection at an early stage. Apex detection is one of the early types of ME detection tasks, but it is often incorrectly grouped as ME and MaE detection in the literature. To overcome the shortcomings of LBP-TOP, which only employs features on three planes, [PERSON] and [PERSON] [11] proposed Cubic-LBP. Six cube faces and nine jets that cross the central pixel make up Cubic-LBP. The sum of the squared differences is then used to compute the differences between frames. This method can also determine how much two histograms differ from one another. For the optical flow images, this is still the most frequently utilised input feature. For the detection task, [PERSON] et al. [12] used Region of Interest (ROI) segmentation to extract features, and they then obtained the optical flow for each ROI image. After that, the Histogram of Oriented Optical Flow (HOOF) was computed using the obtained optical flow. For ME or MaE detection, all observed features are compiled into a feature matrix. Another attribute utilised as an input is the absolute frame difference. Only two absolute image differences were used by [PERSON] et al. [13]; however, this technique needs the face to remain stationary and selects the frame using half the expression's interval--something that is impossible in real life because we never know when a facial expression will conclude. Later, subtle facial movements can be detected using motion-based approaches. For the first time, [PERSON] et al. [8] developed optical strain, a derivative of optical flow, to assess  
Figure 1: Illustration of LGNMNet-RF Overall Architecture. minute motion changes based on the elastic stretches of facial skin tissue. Consideration is given to the total of the strain magnitudes seen over time at various facial locations. Main Directional Maximal Difference (MDMD) [14] was the baseline method for the Micro-Expression Grand Challenge (MEGC) 2020. This encodes the maximal difference magnitude along the significant direction of motion to identify whether an ME or MaE is present. At the moment, the majority of research on ME detection uses explicit visual motion representations, such as optical flow. Researchers used the advantages of deep learning algorithms to extract features and identify MEs. [PERSON] et al. [2] were the first to use a deep learning model for the MEGC 2021 competition. [PERSON] used masked background images to build the Local Contrast Normalisation (LCN) images. Subsequently, distinct lengths of frame skips were implemented: MaE streams consisting of identical 3D-CNNs received larger frame skips, while ME streams received shorter frame skips. The Residual Dense network model first uses global average pooling, and then carries out extra classification. The two 3D-CNNs' weights were pooled to reduce overfitting issues. The first to use the human facial action units (AUs) in conjunction with the AU-based Graph Convolutional Network approach to learn finer spatial information and produce a probability map was [PERSON] et al. [15] RetinaFace was used to crop, align, and conceal the faces' backgrounds in the facial photographs. Therefore, the proposal generation algorithm generated the proposals based on the probability map.