Owing to the development and advancement of artificial intelligence, numerous works have been established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions have been attracting attention from various research communities in the recent few years. In this paper, we first review the processes of a conventional optical-flow-based recognition system. Concisely, it comprises four basic steps: facial landmarks annotations (to detect the face and locate the landmark coordinates), optical flow guided images computation (to describe the dynamic changes on the face), feature extraction (to summarize the features encoded) and emotion class categorization (to build a classification model based on the given training data). Secondly, a few approaches have been implemented to improve the feature extraction part, such as exploiting GAN to generate more image samples. Particularly, several variations of optical flow are computed in order to generate optimal images, which lead to high recognition accuracies. Next, GAN, a combination of Generator and Discriminator, is utilized to generate new “fake” images to increase the sample size. Thirdly, a modified state-of-the-art convolutional neural networks is proposed. In brief, multiple optical flow derived components are adopted in the OFF-ApexNet structure to better represent the facial subtle motion changes. From the experiment results obtained, the additional optical flow information computed does not complement the feature extraction stage, and thus leading to poorer recognition performance. On the other hand, the implementation of GAN to the input data improves the performance in SMIC dataset, by achieving the accuracy of 61.80%, 62.20% and 60.98% for AC-GAN, SAGAN and without GAN images, respectively.

由于人工智能的发展和进步，在人类面部表情识别系统中已经建立了许多作品。同时，近年来，微表达的检测和分类引起了各研究团体的关注。在本文中，我们首先回顾了传统的基于光流的识别系统的过程。简而言之，它包括四个基本步骤：面部地标注释（检测面部并定位地标坐标），光流引导图像计算（以描述面部的动态变化），特征提取（以总结编码的特征）和情感类别分类（基于给定的训练数据构建分类模型）。其次，已经实施了一些方法来改进特征提取部分，例如利用GAN生成更多图像样本。特别地，计算光流的几种变化以便生成最佳图像，这导致高识别精度。接下来，将生成器和鉴别器的组合GAN用于生成新的“伪”图像，以增加样本大小。第三，提出了一种改进的先进的卷积神经网络。简而言之，在OFF-ApexNet结构中采用了多个光学流派生组件，以更好地表示面部的细微运动变化。从获得的实验结果来看，计算出的附加光流信息不能补充特征提取阶段，从而导致识别性能较差。另一方面，