FaceReader is the world’s first tool capable of automatically analyzing facial expressions, providing users with an objective assessment of a person’s emotion.
In 2007 FaceReader 1.0 was released. Since then, a new FaceReader release has been brought to the market on annual basis, with FaceReader 8.1 being the current version released in February of 2020. With every purchase of FaceReader you receive a complete software package with full customer support offered by VicarVision’s partner, Noldus.
FaceReader ClientsFaceReader is used worldwide at more than 500 universities, research institutes, and companies in many markets, such as consumer behavior research, usability studies, psychology, educational research and market research.
Facial ExpressionsHappy, Sad, Angry, Surprised, Scared, Disgusted, ContemptNEW and Neutral.
ValenceA measure of the attitude of the participant (positive vs negative).
ArousalNEWA measure of the activity of the participant (active vs inactive).
Action Units20 of the most common Facial Action Units.
Facial StatesEyes opened/closed, Mouth opened/closed, Eye Brows lowered/neutral/raised.
Global GazeA global gaze direction (left, forward or right) helps to determine attention.
CharacteristicsGender, Age and the presence of Glasses, a Beard and a Moustache.
Head PoseAccurate head pose can be determined from the 3D face model.
3D Face Modeling
Realtime 3D Modeling
FaceReader uses an advanced 3D face modeling technique, with over 500 keypoints. The system is capable of modeling a face in realtime, without any manual initialization needed.
FaceReader Output VisualizationFaceReader contains a wide variety of visualization options, to make the data easily accessible for the researcher.
Continuous Expression Intensities
FaceReader outputs the 6 basic expressions, Happy, Sad, Angry, Surprised, Scared, Disgusted and an extra Neutral state as continuous intensity values between zero and one.
New in FaceReader is the addition of Contempt as the 7th expression.
Action Unit Detection
The six basic emotions are only a fraction of the possible facial expressions. A widely used method for describing the activation of the individual facial muscles is the Facial Action Coding System (Ekman 2002).
FaceReader can detect the 20 most common AUs.
Circumplex Model of Affect
The circumplex model of affect describes the distribution of emotions in a 2D circular space, with arousal and valence dimensions.
Circumplex models (Russel 1980) are commonly used to assess liking in marketing, consumer science, and psychology.
A summary of the expressions during a single analysis can be viewed in a easy understandable pie chart, showing overall responses.
Different subparts of the analysis can be selected to view the summary of the expressions.
FaceReader can automatically classify some key characteristics of your participants.
FaceReader can automatically classify the state of some key parts of the participants face.
All your participants in one project
In FaceReader you can (re)create your complete experiment, adding all your participants to one single project. The Project Analysis Module allows for analysis of the response of groups of participants, towards your stimuli.
Participants can be grouped based on independent variables, like age, gender or any manually entered variable.
Using the numerical project analysis window, the significance of differences between groups or between stimuli can automatically be calculated. To easily see, with a single glance, what the interesting differences are.
Group results can be visualized as interactive barcharts and boxplots. Hovering gives detailed information, quantifying the differences.
Using the temporal project analysis window, you can view your stimuli synced with the average response of you participants.