论文标题
转移双随机图卷积网络，以进行面部微表达识别
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition
论文作者
论文摘要
由于其在谎言检测，犯罪检测和心理咨询中的广泛应用，微观表达的识别引起了人们的关注。为了提高小型微表达数据的识别性能，本文提出了转移的双随机图卷积网络（TDSGCN）模型。我们提出了一种随机图构造方法和双图卷积网络，以从微表达图像中提取更多的区分特征。我们使用转移学习从宏观表达数据中预先培训SGCN。光流算法还集成以提取其时间特征。我们融合了空间和时间特征，以提高识别性能。据我们所知，这是在微观表达识别任务中使用转移学习和图形卷积网络的首次尝试。此外，为了解决数据集的类不平衡问题，我们重点介绍了焦点损耗函数的设计。通过广泛的评估，我们提出的方法在SAMM上实现了最先进的性能，并最近发布了MMEW基准。本文随附我们的代码。
Micro-expression recognition has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. To improve the recognition performance of the small micro-expression data, this paper presents a transferring dual stochastic Graph Convolutional Network (TDSGCN) model. We propose a stochastic graph construction method and dual graph convolutional network to extract more discriminative features from the micro-expression images. We use transfer learning to pre-train SGCNs from macro expression data. Optical flow algorithm is also integrated to extract their temporal features. We fuse both spatial and temporal features to improve the recognition performance. To the best of our knowledge, this is the first attempt to utilize the transferring learning and graph convolutional network in micro-expression recognition task. In addition, to handle the class imbalance problem of dataset, we focus on the design of focal loss function. Through extensive evaluation, our proposed method achieves state-of-the-art performance on SAMM and recently released MMEW benchmarks. Our code will be publicly available accompanying this paper.
