MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted to use the whole face, yet a facial MiE appears in a small region of the face, which makes the extraction of relevant features a hard task. In this paper, we propose a novel deep learning approach that leverages the locality aspect of MiEs by learning spatio-temporal features from local facial regions using a composite architecture of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed solution succeeds to extract relevant local features for MiEs recognition. Experimental results on benchmark datasets demonstrate the highest recognition accuracy of our solution with respect to state-of-the-art methods.

MiE是一种面部无意识反应，反映了人类真实的情感和思想。正常人很难检测到微表情 (MiE)，因为它是一种非常快速且局部的面部反应，强度很低。因此，对于研究人员来说，构建用于 MiE 识别的自动系统是一项具有挑战性的任务。以前的 MiE 识别工作试图使用整个人脸，但面部 MiE 出现在人脸的一小部分区域，这使得相关特征的提取成为一项艰巨的任务。在本文中，我们提出了一种新颖的深度学习方法，该方法通过使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 的复合架构从局部面部区域学习时空特征来利用 MiE 的局部性。所提出的解决方案成功地提取了用于 MiE 识别的相关局部特征。基准数据集的实验结果表明，我们的解决方案相对于最先进的方法具有最高的识别准确度。