Micro-expression recognition (MER) has drawn increasing attention in recent years due to its ability to reveal the true feelings people want to hide. The key challenge in MER is subtle motions, which are hard to capture but crucial for MER. Existing methods usually solve this problem by magnifying all motions in the whole face and temporal sequence. However, micro-expressions (MEs) only involve a few facial areas and several temporal snippets. The all-motion magnification in previous methods cannot precisely capture these local ME motion patterns, and can easily cause spatial as well as temporal distortions, which significantly decrease the MER accuracy. In this paper, we propose an RGB-Frequency Relation Exploration Network (RF-REN), which enhances the subtle motions in refined local ME cues by exploring spatial and temporal relations in both RGB and frequency domains. Specifically, we first decompose the ME video into RGB as well as frequency domains, and conduct temporal division according to different motion stages to cover various ME local patterns. Secondly, we construct an adaptive local-global relation exploration (LGRE) module to explore the local relation cues in the spatial appearance and temporal dynamics in both domains. Finally, we propose an RGB-Frequency routing strategy to fuse the RGB and frequency cues, aiming to aggregate spatial-temporal local-global information and enhance subtle motions for MER. Extensive experiments on three databases (CASME II, SAMM and SMIC) show that the proposed model outperforms other state-of-the-art methods.

A micro-expression (ME) is a kind of involuntary facial expressions, which commonly occurs with subtle intensity. The accurately recognition ME, a. k. a. micro-expression recognition (MER), has a number of potential applications, e.g., interrogation and clinical diagnosis. Therefore, the subject has received a high level of attention among researchers in affective computing and pattern recognition communities. In this paper, we proposed a straightforward and effective deep learning method called uncertainty-aware magnification-robust networks (UAMRN) for MER, which attempts to address two key issues in MER including the low intensity of ME and imbalance of ME samples. Specifically, to better distinguish subtle ME movements, we reconstructed a new sequence by magnifying the ME intensity. Furthermore, a sparse self-attention (SSA) block was implemented which rectifies the standard self-attention with locality sensitive hashing (LSH), resulting in the suppression of artefacts generated during magnification. On the other hand, for the class imbalance problem, we guided the network optimization based on the confidence about the estimation, through which the samples from rare classes were allotted greater uncertainty and thus trained more carefully. We conducted the experiments on three public ME databases, i.e., CASME II, SAMM and SMIC-HS, the results of which demonstrate improvement compared to recent state-of-the-art MER methods.