FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Abstract

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion, but all of existing datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA. One hundred and twenty-two participants were recorded in real-world conditions. 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify the re-defined action units according to Facial Action Coding System. Each action unit is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of FEAFA for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.