AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing
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DOI：
10.1109/tvcg.2021.3114784
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发表时间：
2021-07
影响因子：
5.2
通讯作者：
Hamza Elhamdadi;Shaun J. Canavan;Paul Rosen
中科院分区：
文献类型：
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作者：
Hamza Elhamdadi;Shaun J. Canavan;Paul Rosen
我们提出了一种利用拓扑数据分析的方法来研究用于情感计算的人脸姿势的结构，即识别人类情感的过程。该方法使用具有多个拓扑距离度量、降维技术和脸部分区(例如，眼睛、鼻子、嘴巴等)的不同情感的条件比较，所述不同情感既各自又与时间无关。结果证实，我们的基于拓扑的方法捕捉到了已知的模式、情感之间的差异以及个体之间的差异，这是机器朝着更健壮和更可解释的情感识别迈出的重要一步。
We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions, both respective and irrespective of time, with multiple topological distance metrics, dimension reduction techniques, and face subsections (e.g., eyes, nose, mouth, etc.). The results confirm that our topology-based approach captures known patterns, distinctions between emotions, and distinctions between individuals, which is an important step towards more robust and explainable emotion recognition by machines.