FaceReader automatically analyzes a selection of 20 commonly used Action Units, such as raising of cheeks, wrinkling of nose, dimpling, and lip tightening. Action Units are the actions of individual muscles or groups of muscles. When an action is active, its intensity is displayed in 5 categories A (Trace), B (Slight), C (Pronounced), D (Severe), E (Max). Output will be presented to you on this scale with different colors and can be exported for further analysis in Excel, The Observer XT, or another program of your choice.
Export in detailed log as numerical values is possible, as well as export in real-time, using the FaceReader API, allowing you to create adaptive applications driven by facial action units. This module offers you valuable information in a minimal amount of time. Especially when you are analyzing a large amount of video recordings, this module will help you advance your research.
Detecting boredom, confusion, and interest
In FaceReader 7.1, we introduce the analysis of three commonly occurring affective attitudes, namely: interest, boredom, and confusion. Unlike regular facial expressions, these affective attitudes are computed over a window of time (typically 2-5 seconds), rather than per time frame. In addition, some of these affective attitudes also take into account certain additional facial cues like nodding or head shaking, that are also internally computed over the analysis history.
These analyses are available on an experimental basis.
Curious what Action Units actually look like? We have listed the 20 Action Units offered in the most recent version of FaceReader, of which you see three examples below, as well as some frequently occurring or difficult action unit combinations.
Contact us for more information! Our sales representatives are happy to provide you with 'Validation Action Unit Module' (by Tim den Uyl). This document presents a validation of the FaceReader Action Unit Module by evaluating the performance on an annotated dataset. It provides the agreement between FaceReader classifications and the coded dataset, as well as an evaluation of the performance of the individual AU classifiers.
FaceReader methodology white paper
Request the FREE FaceReader methodology note to learn more about facial expression analysis theory.
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