阅读 CVPR 2018 papers on detecting facial action units

最新推荐文章于 2024-06-02 09:36:33 发布

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最新推荐文章于 2024-06-02 09:36:33 发布 · 1.2k 阅读

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文章标签：

#人脸表情 #人脸识别 #动作识别 #情感计算 #物体检测

本文概述了CVPR 2018上关于面部动作单元检测的研究，包括弱监督聚类、对抗训练、卷积神经网络滤波器优化和先验概率分类器学习的方法。研究中提到的瓶颈在于数据集的质量和规模，如AFEW、EmotioNet和BP4D。注意力机制和配置学习（如贝叶斯网、CRF）在解决AU的空间和时间定位及识别方面发挥了重要作用。CNN是当前的主要识别工具，结合注意力机制能提升性能。

List

1. Learning Facial Action Units From Web Images With Scalable Weakly Supervised Clustering
2. Weakly Supervised Facial Action Unit Recognition Through Adversarial Training
3. Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
4. Classifier Learning With Prior Probabilities for Facial Action Unit Recognition

Weakly-Supervised Attention and Relation Learning for Facial Action Unit Detection. PAMI submission.

CRF for learning relation.

Dataset

AFEW dataset: collected from Web, mostly dark, can be wearing glasses.

EmotioNet

BP4D

Thoughts

This is a field already with a lot of researches going on. The bottleneck is still the dataset. BP4D is comprehensive but sort of small-scale. The EmotioNet is large-scale yet i