Towards the dataset for analysis and recognition of facial expressions intensity
Marina Tiuleneva, Emanuele Castano, Radosław Niewiadomski · 2024
We propose a novel dataset for studying and modeling facial expression intensity. Facial expression intensity recognition is a rarely discussed challenge, likely stemming from a lack of suitable datasets. Our dataset has been created by extracting facial expressions from actors across twelve fiction films, followed by crowd-sourced online annotation of the expression intensity and variability levels. It consists of over 400 automatically extracted video segments ranging from 3 to 5 seconds, as well as annotations and facial landmarks. We also present preliminary statistics derived from this dataset.