Enhancing Micro-Expression Analysis Performance by Effectively Addressing Data Imbalance

Micro-expressions (MEs) are involuntary and quickly displayed facial expressions that reveal subtle psychological activities. Most previous research typically focused on two separate tasks: micro-expression spotting and recognition. We aim to propose a high-precision "spotting+recognition" method that can spot ME intervals from long videos and recognize their emotional categories. Due to the occurrence sparsity of MEs, there is a significant imbalance between the number of micro-expression intervals and non-micro-expression intervals in long videos. This imbalance makes it challenging for models trained using conventional strategies to distinguish true MEs from noise samples caused by head movements, blinking, and macro-expressions, resulting in a high false-positive-rate and reducing the overall performance. We reduce the number of smooth segments to alter the data distribution within the non-micro-expression (non-ME) category. This adjustment enables the model to focus more on the subtle differences between noise samples and ME samples. To achieve this, we design an ingenious training data preparation strategy: using false positive samples from the initial spotting results as non-ME category samples, and using true positive and false negative samples from the initial spotting as emotion category samples. These are combined as the training data, creating a recognition model capable of both emotion classification and non-ME category determination. Additionally, we propose a three-stage micro-expression analysis method, including ME spotting, ME recognition and non-ME intervals removal module. Our method is validated through five-fold cross-validation experiments on the CAS(ME)² and SAMM Long Video datasets, achieving a overall STRS metric of 0.16, which significantly outperformed baseline methods and demonstrated the effectiveness of our approach.