AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit
This toolkit addresses the problem of real-time, multi-face facial expression analysis in challenging conditions for researchers and developers in computer vision and human-computer interaction, representing an incremental enhancement.
The paper introduces AFFDEX 2.0, a toolkit for real-time facial expression analysis that tackles tasks such as 3D head pose estimation, facial Action Unit detection, and emotion recognition, including new states like sentimentality and confusion, with improvements in accuracy and efficiency over its predecessor.
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.