https://scholars.lib.ntu.edu.tw/handle/123456789/606283
標題: | Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions | 作者: | Huang T Hsu S TSUNG-REN HUANG LI-CHEN FU |
關鍵字: | Computational modeling;Data augmentation;Data models;Databases;emotion recognition;Emotion recognition;emotional intensity;face morphing;Face recognition;Faces;facial emotional expressions;Training;Behavioral research;Desirable features;Emotional recognition;Face Morphing;Facial emotions;Facial Expressions;Model training | 公開日期: | 2021 | 來源出版物: | IEEE Transactions on Affective Computing | 摘要: | Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. CCBYNCND |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110880579&doi=10.1109%2fTAFFC.2021.3096922&partnerID=40&md5=fbc04d839d10b7ea581af82c2bc98da3 https://scholars.lib.ntu.edu.tw/handle/123456789/606283 |
ISSN: | 19493045 | DOI: | 10.1109/TAFFC.2021.3096922 |
顯示於: | 心理學系 |
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