# Lightweight Micro-Expression Recognition on Composite Database
## 1 Introduction  
Micro-expressions are subtle, spontaneous and quick facial expressions with duration less than 0.5 seconds and involving only certain face muscles [1]. They occur when a person tries to hide or withhold their emotions from being expressed, which suggests genuine hidden emotions rather than normal facial expressions or verbal signals [2]. Micro-expression recognition (MER) aims to automatically identify the hidden emotions such as Happiness, Sadness or Disgust from the micro-expressions. MER has potential usage in a variety of fields such as security, education and public health. However, the quick and delicate nature of micro-expressions makes it challenging to recognize them. For instance, US coast guards who were trained to recognized micro-expressions using the Micro-Expression Training Tool (METT) pioneered by [PERSON] achieved no more than 50% accuracy [3]. Earlier works on Micro-Expression Recognition (MER) were more focused on crafting features that can best represent the subtle muscle movements in micro-expressions such as variants of Local Binary Patterns (LBP) [4, 5] and Histogram of Gradients (HOG) [6]. To increase MER accuracy, many researchers then turn to optical flow and optical strain features with the aim of including temporal-based information from the quick facial movements in micro-expressions [7, 8]. However, these features require pre-extraction either manually orautomatically which increases MER complexity, and as such are not practical for a real-life recognition system. Along with the success of deep learning methods' applications in Facial Expression Recognition (FER), trends in MER are now geared more towards deep learning approaches, yet most of the implementation in MER utilizes OF features [9; 10; 11; 12; 13] making them more complex. Similarly, while [14] proposed an end-to-end micro-expression spotting and recognition system, it requires an additional synthetic OF generation step to augment the training dataset. Other implementations using Dynamic Images (DI) [15; 16; 17] and facial graph [18] required pre-constructing the input features. Due to the insufficient samples in the micro-expression databases for deep learning methods, many have employed the transfer learning technique [19; 20], but the effect of transfer learning has not been explored and explained, while the MER accuracies are still low. Additionally, works such as [12; 13; 17] do not generalize well as they are trained and validated using one or two micro-expression databases only having similar demographic and data collection conditions. A better evaluation method proposed in Micro-Expression Grand Challenge (MEGC) 2019 involves conducting Leave-One-Subject-Out (LOSO) cross-validation on the composite micro-expression datasets using Unweighted F1 (UF1) score metric designed to alleviate class imbalance and model generalization problems [21]. MER using this composite dataset is an ongoing research problem with the best performance so far by MTM-NET only having attained 0.864 (Unweighted F1) UF1 Score and 0.85 (Unweighted Average Recall) UAR score [15], due to the more challenging dataset with diverse demography and sample collection methods as well as an imbalanced dataset. To understand how to improve MER on the composite dataset [22] has discovered the importance of lower model and input complexity when utilizing deep learning on the composite dataset, whereas deeper models are more suitable for single database evaluation. Model complexity refers to the number of parameters in the model that consists of all the learnable weights and biases, whereas input complexity is the input resolution [10]. However, research on reducing both the input and model complexity is still lacking so far. In this work, a MER model is proposed that is based on deep learning with low input and low model complexity to overcome the small and imbalanced dataset as well as to learn salient features from the subtle and spontaneous micro-expressions with improved accuracy on the composite dataset.