Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions.

ABSTRACT: Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). 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 (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-stand

SUBMITTER: Kuntzler T 

PROVIDER: S-EPMC8131548 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature