EIRN: A Method for Emotion Recognition Based on Micro-Expressions

Micro-expressions are involuntary facial movements that reveal a person's true emotions when attempting to conceal them. These expressions hold significant potential for various applications. However, due to their brief duration and subtle manifestation, detailed features are often obscured by redundant information, making micro-expression recognition challenging. Previous studies have primarily relied on convolutional neural networks (CNNs) to process high-resolution images or optical flow features, but the complexity of deep networks often introduces redundancy and leads to overfitting. In this paper, we propose EIRN, a novel method for micro-expression recognition. Unlike conventional approaches, EIRN explicitly separates facial features of different granularities, using shallow networks to extract sparse features from low-resolution greyscale images, while treating onset–apex pairs as Siamese samples and employing a Siamese neural network (SNN) to extract dense features from high-resolution counterparts. These multigranularity features are then integrated for accurate classification. To mitigate overfitting in fine-grained feature extraction by the SNN, we introduce an attention module tailored to enhance crucial feature representation from both onset and apex frames during training. Experimental results on single and composite datasets demonstrate the effectiveness of our approach and its potential for real-world applications.