当前位置: X-MOL 学术arXiv.cs.AI › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
arXiv - CS - Artificial Intelligence Pub Date : 2021-03-04 , DOI: arxiv-2103.02854
Vassilios Vonikakis, Dexter Neo, Stefan Winkler

Emotion recognition and understanding is a vital componentin human-machine interaction. Dimensional models of affectsuch as those using valence and arousal have advantages overtraditional categorical ones due to the complexity of emo-tional states in humans. However, dimensional emotion an-notations are difficult and expensive to collect, therefore theyare still limited in the affective computing community. To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space,while achieving augmentation factors of at least 20x or more.

中文翻译:

Morphset:使用脸部变形来增强带有维度影响标签的分类情感数据集

情感识别和理解是人机交互中的重要组成部分。由于人类情绪状态的复杂性,情感的维数模型(例如使用价和唤醒的维数模型)具有优于传统分类法的优势。但是,维情感注释难以收集且昂贵,因此在情感计算社区中仍然受到限制。为了解决这些问题,我们提出了一种方法,该方法可以使用人脸变形从现有类别情感数据集中生成合成图像,并完全控制所得样本分布以及外接空间中的尺寸标签,同时实现至少20倍的增强因子或者更多。
更新日期:2021-03-05
down
wechat
bug