# LGNMNet-RF: Micro-Expression Detection Using Motion History Images
###### Abstract  
Micro-expressions are very brief, involuntary facial expressions that reveal hidden emotions, lasting less than a second, while macro-expressions are more prolonged facial expressions that align with a person's conscious emotions, typically lasting several seconds. Micro-expressions are difficult to detect in lengthy videos because they have tiny amplitudes, short durations, and frequently coexist alongside macro-expressions. Nevertheless, micro- and macro-expression analysis has sparked interest in researchers. Existing methods use optical flow features to capture the temporal differences. However, these optical flow features are limited to two successive images only. To address this limitation, this paper proposes LGNMNet-RF, which integrates a Lite General Network with MagFace CNN and a Random Forest classifier to predict micro-expression intervals. Our approach leverages Motion History Images (MHI) to capture temporal patterns across multiple frames, offering a more comprehensive representation of facial dynamics than optical flow-based methods, which are restricted to two successive frames. The novelty of our approach lies in the combination of MHI with MagFace CNN, which improves the discriminative power of facial micro-expression detection, and the use of a Random Forest classifier to enhance interval prediction accuracy. The evaluation results show that this method outperforms baseline techniques, achieving micro-expression F1-scores of 0.3019 on CAS(ME)\({}^{2}\) and 0.3604 on SAMM-LV. The results of our experiment indicate that MHI offers a viable alternative to optical flow-based methods for micro-expression detection. micro-expression detection; macro-expression detection; motion-energy images; motion history images; computer vision; artificial intelligence; deep learning +
Footnote †: journal: _articles  
ME interval, ME classification will receive input from non-expression intervals, leading to the significant misclassification of expressions. The movements of several facial muscles constitute a facial expression. This is a form of nonverbal communication that could reveal someone's emotional state. However, since people can purposefully display particular facial expressions to conceal their emotions (such as faking a smile), facial expressions are not always an accurate indicator of a person's emotional state. Subtle facial movements that may occur during the suppression of emotions are referred to as facial MEs, and regular facial expressions as facial Macro-Expressions (MaEs). MaE is a frequent and easily recognised facial expression with a stronger intensity and a longer duration of around 0.5 to 4.0 s [2]. On the other hand, an involuntary ME is more subdued, often lasts within 0.5 s, and manifests when an individual tries to conceal their true emotions [3]. Since ALS patients have limited muscle movements, our research focuses on facial ME, a slight, involuntary movement of the face that lasts less than 0.5 s. MEs are analysed to improve the communication process with ALS patients. Two primary steps in analysing expressions in ALS patients are detection and recognition. While the detection task requires determining the expression's interval from long recordings, the recognition task classifies the ME clips into many emotion categories [4]. The point of interest in this study is that ME detection involves identifying frames showing the precise onset (when the expression begins), apex (when the expression is at its peak intensity), and offset (when the expression ends) of an expression within a video sequence to capture and analyse these brief, subtle facial movements accurately. While optical flow images are frequently used to detect variations in the colour and intensity of facial pixels between frames to identify ME, temporal details are frequently missed by 2D deep learning algorithms. Therefore, this study proposed employing Motion History Images (MHI) [5] for ME and MaE detection.