Abstract

Facial expression recognition for frontal faces has become a well-established research area in the last two decades. However, non-frontal facial expression recognition hasn’t been paid much attention until recently. In this paper, we propose an MVFE-LightNet (Multi-View Facial Expression Light Weight Network) for multi-view facial expression recognition. To this end, we first applied MTCNN for facial detection and alignment and then did preprocessing like normalization and data augmentation. Finally, we put the images into MVFE-LightNet to extract sub-space features of facial expressions with various poses. A depthwise separable residual convolution module architecture was designed to reduce the parameters of the model and lessen the chance of overfitting. Experiments were implemented on Radboud Faces Database and BU-3DFE dataset. We demonstrated that our method could effectively improve the recognition accuracy, and achieved the accuracy of 95.6% and 88.7% respectively for the Radboud and BU-3DFE.

中文翻译：

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利用多视角面部表情轻量网络的多视角面部表情识别

摘要

在过去的二十年中，面部表情识别已成为一个成熟的研究领域。然而，直到最近，非正面面部表情识别才引起人们的关注。在本文中，我们提出了一种用于多视图面部表情识别的MVFE-LightNet（多视图面部表情轻量网络）。为此，我们首先将MTCNN用于面部检测和对齐，然后进行诸如归一化和数据增强之类的预处理。最后，我们将图像放入MVFE-LightNet中，以提取具有各种姿势的面部表情的子空间特征。设计了深度可分离残差卷积模块架构，以减少模型的参数并减少过度拟合的机会。实验在Radboud Faces数据库和BU-3DFE数据集上进行。