肌电图
面部肌电图
计算机科学
人工智能
计算机视觉
物理医学与康复
医学
作者
Monica Perusquía-Hernández,Saho Ayabe-Kanamura,Kenji Suzuki,Shiro Kumano
出处
期刊:Human Factors in Computing Systems
日期:2019-05-02
被引量:15
标识
DOI:10.1145/3290605.3300379
摘要
Positive experiences are a success metric in product and service design. Quantifying smiles is a method of assessing them continuously. Smiles are usually a cue of positive affect, but they can also be fabricated voluntarily. Automatic detection is a promising complement to human perception in terms of identifying the differences between smile types. Computer vision (CV) and facial distal electromyography (EMG) have been proven successful in this task. This is the first study to use a wearable EMG that does not obstruct the face to compare the performance of CV and EMG measurements in the task of distinguishing between posed and spontaneous smiles. The results showed that EMG has the advantage of being able to identify covert behavior not available through vision. Moreover, CV appears to be able to identify visible dynamic features that human judges cannot account for. This sheds light on the role of non-observable behavior in distinguishing affect-related smiles from polite positive affect displays.
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