论文标题
通过同时进行动作单元检测和特征聚合，基于目标类的微表达识别
Objective Class-based Micro-Expression Recognition through Simultaneous Action Unit Detection and Feature Aggregation
论文作者
论文摘要
微表达识别（MER）是一项艰巨的任务，因为在面部的不同作用区域发生了细微的变化。面部作用区域的变化被形成为行动单位（AUS），而微观表达的AU可以看作是合作团体活动中的参与者。在本文中，我们为基于目标类的MER提出了一种新颖的深神经网络模型，该模型同时检测AUS并通过图卷积网络（GCN）将AU级特征汇总到微表达级表示中。具体来说，我们在AU检测模块中提出了两种新策略，以进行更有效的AU特征学习：注意机制和平衡的检测损耗函数。借助这两种策略，在统一模型中为所有AU提供了特征，从而消除了误差地标的检测过程，并为每个AU提供了乏味的单独培训。此外，我们的模型结合了一个量身定制的基于目标类的AU知识图，该图促进了GCN将AU级特征汇总到微表达级别的特征表示中。对MEGC 2018中两项任务的广泛实验表明，我们的方法大大优于MER中最新的方法。此外，我们还报告了基于单个模型的微表达AU检测结果。
Micro-Expression Recognition (MER) is a challenging task as the subtle changes occur over different action regions of a face. Changes in facial action regions are formed as Action Units (AUs), and AUs in micro-expressions can be seen as the actors in cooperative group activities. In this paper, we propose a novel deep neural network model for objective class-based MER, which simultaneously detects AUs and aggregates AU-level features into micro-expression-level representation through Graph Convolutional Networks (GCN). Specifically, we propose two new strategies in our AU detection module for more effective AU feature learning: the attention mechanism and the balanced detection loss function. With those two strategies, features are learned for all the AUs in a unified model, eliminating the error-prune landmark detection process and tedious separate training for each AU. Moreover, our model incorporates a tailored objective class-based AU knowledge-graph, which facilitates the GCN to aggregate the AU-level features into a micro-expression-level feature representation. Extensive experiments on two tasks in MEGC 2018 show that our approach significantly outperforms the current state-of-the-arts in MER. Additionally, we also report our single model-based micro-expression AU detection results.
