Micro-Expression Recognition Using Uncertainty-Aware Magnification-Robust Networks

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.     

微反应在化学课堂情感态度与价值观目标评定中的应用

《普通高中化学课程标准（实验）》把情感、态度与价值观作为重要的教学目标提出来,并提出了相应的评定建议,但是没有一个具体的、客观的评定方式。人们内心的情感、态度与价值观在受到有效刺激时,可以通过外显的微反应表达出来。在化学课堂教学中,教师可以采用微反应来评定第3维目标的达成情况。在运用微反应评定时,要求教师具有敏锐的观察力、综合分析的能力和“一心二用”的能力,且按确定观察目的、制定观察记录表、观察记录、评定4个步骤进行。