Abstract
Owing to the development and advancement of artificial intelligence, numerous works were established in the human facial expression recognition system. Meanwhile, the detection and classification of micro-expressions are attracting attentions 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, which comprised of facial landmarks annotations, optical flow guided images computation, features extraction and emotion class categorization. Secondly, a few approaches have been proposed 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 to lead to high recognition accuracy. 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. To verify the effectiveness of the the proposed method, the results are evaluated on spontaneous micro-expression databases, namely SMIC, CASME II and SAMM. Both the F1-score and accuracy performance metrics are reported in this paper.
Abstract (translated)
随着人工智能的发展和进步，人类面部表情识别系统的研究工作层出不穷。同时，近年来微表达的检测和分类也引起了各研究领域的关注。本文首先回顾了传统的基于光流的识别系统的工作过程，包括人脸标志标注、光流引导图像计算、特征提取和情感分类。其次，提出了一些改进特征提取部分的方法，如利用GaN生成更多的图像样本。特别地，计算了光流的几种变化，以产生最佳图像，从而获得较高的识别精度。接下来，Gan（发生器和鉴别器的组合）用于生成新的“假”图像，以增加样本大小。第三，提出了一种改进的最先进的卷积神经网络。为了验证该方法的有效性，我们在SMIC、CASMEII和SAMM等自发微表达数据库上对实验结果进行了评价。本文报告了F1成绩和准确度绩效指标。
URL
https://arxiv.org/abs/1904.01748
