Emotion Review, Volume 14, Issue 1, Page 84-95, January 2022. Emotional stimuli such as images, words, or video clips are often used in studies researching emotion. New sets are continuously being published, creating an immense number of available sets and complicating the task for researchers who are looking for suitable stimuli. This paper presents the KAPODI-database of emotional stimuli sets that are freely available or available upon request. Over 45 aspects including over 25 key set characteristics have been extracted and (...) listed for each set. The database facilitates finding of and comparison between individual sets. It currently contains sets published between 1963 and 2020. A searchable online version allows users to select specific set characteristics and to find matching sets accordingly, as well as to add new published sets. (shrink)

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition. Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems with human emotion recognition for standardized (...) posed facial expressions and for non-standardized acted facial expressions. For the standardized images, all three systems classify basic emotions accurately and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions. (shrink)