论文标题
使用光流和EVM的多流卷积神经网络，用于微表达识别
A Multi-stream Convolutional Neural Network for Micro-expression Recognition Using Optical Flow and EVM
论文作者
论文摘要
微表达（ME）识别在广泛的应用中，尤其是在公共安全和心理治疗中起着至关重要的作用。最近，传统方法过于依赖机器学习设计，并且由于其持续时间短，强度较低，因此识别率不足以实现其实际应用。另一方面，由于数据库不平衡等问题，基于深度学习的某些方法也无法获得高精度。为了解决这些问题，我们在本文中为我设计了多流卷积神经网络（MSCNN）。具体而言，我们采用EVM和光学流程来放大和可视化MES中微妙的运动变化，并从光流图像中提取口罩。然后，我们将蒙版，光流图像和灰度图像添加到MSCNN中。之后，为了克服数据库的不平衡，我们在神经网络的致密层之后添加了一个随机的过度采样器。最后，在两个公共ME数据库中进行了广泛的实验：Casme II和SAMM。与许多最新的方法相比，我们的方法取得了更有希望的识别结果。
Micro-expression (ME) recognition plays a crucial role in a wide range of applications, particularly in public security and psychotherapy. Recently, traditional methods rely excessively on machine learning design and the recognition rate is not high enough for its practical application because of its short duration and low intensity. On the other hand, some methods based on deep learning also cannot get high accuracy due to problems such as the imbalance of databases. To address these problems, we design a multi-stream convolutional neural network (MSCNN) for ME recognition in this paper. Specifically, we employ EVM and optical flow to magnify and visualize subtle movement changes in MEs and extract the masks from the optical flow images. And then, we add the masks, optical flow images, and grayscale images into the MSCNN. After that, in order to overcome the imbalance of databases, we added a random over-sampler after the Dense Layer of the neural network. Finally, extensive experiments are conducted on two public ME databases: CASME II and SAMM. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.
