论文标题
动作单位发生模式对检测的影响
Impact of Action Unit Occurrence Patterns on Detection
论文作者
论文摘要
检测动作单元是面部分析的重要任务，尤其是在面部表达识别中。这部分是由于可以将表达分解为多个动作单元的想法。在本文中，我们研究了作用单元的发生模式对检测动作单位的影响。为了促进这项调查，我们在两个最先进的面部数据库中回顾了艺术文献，以进行AU检测，这些数据库通常用于此任务，即DISFA和BP4D。从本文献综述中，我们的发现表明，行动单元的发生模式强烈影响评估指标（例如F1二进制）。除文献综述外，我们还进行了多动作单元检测，并提出了一种新的方法来使用发生模式来显式训练深层神经网络，以提高动作单元检测的准确性。这些实验验证了该动作单位模式直接影响评估指标。
Detecting action units is an important task in face analysis, especially in facial expression recognition. This is due, in part, to the idea that expressions can be decomposed into multiple action units. In this paper we investigate the impact of action unit occurrence patterns on detection of action units. To facilitate this investigation, we review state of the art literature, for AU detection, on 2 state-of-the-art face databases that are commonly used for this task, namely DISFA, and BP4D. Our findings, from this literature review, suggest that action unit occurrence patterns strongly impact evaluation metrics (e.g. F1-binary). Along with the literature review, we also conduct multi and single action unit detection, as well as propose a new approach to explicitly train deep neural networks using the occurrence patterns to boost the accuracy of action unit detection. These experiments validate that action unit patterns directly impact the evaluation metrics.
