Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.

ABSTRACT: Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings o

SUBMITTER: Haines N 

PROVIDER: S-EPMC6363175 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature