PRNet: A Priori Embedded Network for Real-World Blind Micro-Expression Recognition

Micro-expressions, fleeting and often unnoticed facial cues, hold the key to uncovering concealed emotions, offering significant implications for understanding emotions, cognition, and psychological processes. However, micro-expression information capture presents challenges due to its instantaneous and subtle nature. Furthermore, it is affected by unpredictable degradation factors such as device performance and weather, and model degradation issues persist in real scenarios, and directly training deep networks or introducing image restoration networks yields unsatisfactory results, hindering the development of micro-expression recognition in real-world applications. This study aims to develop an advanced micro-expression recognition algorithm to promote the research of micro-expression applications in psychology. Firstly, Generative Adversarial Networks (GANs) are employed to build high-quality micro-expression generation models, which are then used as prior decoders to model micro-expression features. Subsequently, the GAN priors of deep neural networks are fine-tuned using low-quality facial micro-expression images. The designed micro-expression GAN module ensures that the generation of latent codes and noise inputs suitable for micro-expression GAN blocks from the deep and shallow features of deep neural networks. This approach controls the reconstruction of facial structure, local details, and accurate expressions to enhance the stability of subsequent recognition networks. Additionally, a Multi-Scale Dynamic Cross-Domain (MSCD) module is proposed to dynamically adjust the input of reconstructed features to different task representation layers. Doing so effectively integrates reconstructed features and improves the micro-expression recognition performance. Experimental results demonstrate that our method consistently achieves superior performance on multiple datasets, achieving particularly significant performance improvements in micro-expression recognition for severely degraded facial images in real scenarios.