In this paper we introduce AFFDEX 2.0 - a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [1], that is capable of tracking efficiently faces at more challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.
@article{arxiv.2202.12059,
title = {AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit},
author = {Mina Bishay and Kenneth Preston and Matthew Strafuss and Graham Page and Jay Turcot and Mohammad Mavadati},
journal= {arXiv preprint arXiv:2202.12059},
year = {2022}
}