Emotion analysis

FaceReader

To gain accurate and reliable data about facial expressions, FaceReader is the most robust automated system that will help you out.

  • Clear insights into the effect of different stimuli on emotions

  • Very easy-to-use: save valuable time and resources

  • Easy integration with eye tracking data and physiology data

 
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Trusted by researchers around the world

 

FaceReader benefits your work

Many researchers have turned towards using automated facial expression analysis software to better provide an objective assessment of emotions. FaceReader software is fast, flexible, objective, accurate, and easy to use. It immediately analyzes your data (live, video, or still images), saving valuable time. The option to record audio as well as video makes it possible to hear what people have been saying – for example, during human-computer interactions, or while watching stimuli.

 
 


Always the best fit for measuring emotions

FaceReader is the most robust automated system for the recognition of a number of specific properties in facial images, including the six basic or universal expressions: happy, sad, angry, surprised, scared, and disgusted. Additionally, FaceReader can recognize a 'neutral' state and analyze 'contempt'.

Whether your test participant is a baby, a child, an adult, or an older person, FaceReader adjusts the analysis to the model that best fits your research.

 


How FaceReader works

  1. Finds a face with the popular Viola-Jones algorithm
  2. Makes an accurate 3D face modeling using > 500 key points (mesh) as well as the texture of the face
  3. Uses the AI technique Deep Learning to analyze the face, even if a part of it is hidden
  4. Classifies the emotional expressions with artificial neural network

These steps result in outcomes such as basic expressions, custom expressions, head orientation, gaze direction, person characteristics, valence and arousal, Action Units, heart rate and heart rate variability, audio, and consumption behavior.

 
 


Customer quote

FaceReader is the most reliable software tool for facial expression analysis (source).

 


What you should know about FaceReader, our facial expression recognition tool

According to a recent validation study, FaceReader 6 shows the best performance out of the major software tools for emotion classification currently available, with an average of 88% [1]. FaceReader 8.1 achieves an even higher score, with 96% [2].

FaceReader and its reliability performance emotions

References

  1. Stöckli, S.; Schulte-Mecklenbeck, M.; Borer, S. & Samson, A.C. (2018) Facial expression analysis with AFFDEX and FACET: A validation study. Behavior Research Methods, 50 (4), 1446-1460.
  2. The average performance of FaceReader 8.1 was measured with the datasets ADFES and WSEFEP.
 
 


Statistics Facereader for facial expression analysis

FaceReader is used worldwide at more than 1,000 universities (including 6 out of 8 Ivy League universities), research institutes, and companies in many markets such as psychology, consumer research, user experience, human factors, and neuromarketing.

 


Who uses it?

FaceReader is used worldwide at more than 1,000 universities, research institutes, and companies in many markets, such as consumer behavior research, usability studies, psychology, educational research, and market research. The software has been used for over a decade now. 

All kind of (research) questions can be answered by using FaceReader:

  • Psychology - how do people respond to particular stimuli, e.g. in fear research?
  • Education - observing students’ facial expressions can support the development of effective educational tools.
  • Human-Computer Interaction - facial expressions can provide valuable information about user experience.
  • Usability testing - emotional expressions can indicate the ease of use and efficiency of user interfaces.
  • Market research - how do people respond to a commercial’s new design?
  • Consumer behavior - how do participants in a sensory panel react to a stimulus?

Many researchers have discovered FaceReader as a tool for their research. For example to find out whether emotions distract people with eating disorders, the value of facial expression analysis in advertisements, or how emotion analysis can be beneficial to researchers in decision making. 

Are you next?

 
 


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