In facial action unit (AU) recognition tasks, regional feature learning and AU relation modeling are two effective aspects which are worth exploring. However, the limited representation capacity of regional features makes it difficult for relation models to embed AU relationship knowledge. In this paper, we propose a novel multi-level adaptive ROI and graph learning (MARGL) framework to tackle this problem. Specifically, an adaptive ROI learning module is designed to automatically adjust the location and size of the predefined AU regions. Meanwhile, besides relationship between AUs, there exists strong relevance between regional features across multiple levels of the backbone network as level-wise features focus on different aspects of representation. In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level. Experiments on BP4D and DISFA demonstrate the proposed MARGL significantly outperforms the previous state-of-the-art methods.

翻译：在面部行动股(AU)识别任务中,区域特征学习和非盟关系建模是值得探讨的两个有效方面,然而,区域特征的代表性有限,使得难以建立将非盟关系知识嵌入关系模式;在本文件中,我们提议建立一个新的多层次适应性ROI和图解学习框架,以解决这一问题;具体地说,一个适应性ROI学习模块旨在自动调整预先界定的非盟区域的位置和规模;同时,除了非盟之间的关系外,骨干网络多个层次的区域特征之间有着很强的相关性,因为其高度特征侧重于代表性的不同方面;为了同时纳入非盟内部关系和非盟区域一级的区域关联性,将绘制一个多层次的非盟关系图,并进行图解演,以进一步加强非盟各级的区域特征;对BP4D和DISFA的实验表明,拟议的MAGL大大超越了先前的先进方法。