HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning

• URL: http://arxiv.org/abs/2009.10892v3
• Date: Thu, 29 Apr 2021 02:04:23 GMT
• Title: HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning
• Authors: Ziqiang Shi and Liu Liu and Zhongling Liu and Rujie Liu and Xiaoyu Mi and and Kentaro Murase
• Abstract summary: We propose a novel framework for the AU detection from a single input image. Our algorithm uses facial landmarks to detect the features of local AUs. Our experiments on the challenging BP4D and DISFA benchmarks yield F1-scores of 63.7% and 61.8% respectively.
• Score: 12.450173086494944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of facial action units (AUs) has been studied as it has the competition due to the wide-ranging applications thereof. In this paper, we propose a novel framework for the AU detection from a single input image by grasping the \textbf{c}o-\textbf{o}ccurrence and \textbf{m}utual \textbf{ex}clusion (COMEX) as well as the intensity distribution among AUs. Our algorithm uses facial landmarks to detect the features of local AUs. The features are input to a bidirectional long short-term memory (BiLSTM) layer for learning the intensity distribution. Afterwards, the new AU feature continuously passed through a self-attention encoding layer and a continuous-state modern Hopfield layer for learning the COMEX relationships. Our experiments on the challenging BP4D and DISFA benchmarks without any external data or pre-trained models yield F1-scores of 63.7\% and 61.8\% respectively, which shows our proposed networks can lead to performance improvement in the AU detection task.

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