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

FaceReader
 
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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.

 
 


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. Face finding – Finds a face using a Deep Learning based face finding algorithm
  2. Face modeling – Makes an accurate artificial face model using almost 500 key points
  3. Face classification – Classifies the expressions with artificial neural networks

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

 
 


Customer quote

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

 


Accurate emotion classification

According to a validation study using ADFES, FaceReader 9 delivers accurate performance for emotion classification, with an average accuracy of 99% [1].

Facereader 9 Average Performance

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

References

  1. The average performance of FaceReader 9 was measured with the datasets Amsterdam Dynamic Facial Expression Set (ADFES).
 
 


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?

  
 


Relevant blogs

SUKIPANI: The magic word for making a smile

SUKIPANI: The magic word for making a smile

The SUKIPANI smile is an exercise to train the muscles you use while smiling. Dr. Sugahara explains the effect of the movements of the muscles and uses FaceReader to analyze the smiles.
The relevance of emotions on the evaluation of a transactional website

The relevance of emotions on the evaluation of a transactional website

A UX test is a method of evaluating a product or service by having users test it. Researchers studied how emotions influence opinions about a website by conducting UX tests.
Measuring the intensity of emotional response to political advertisement videos

Measuring the intensity of emotional response to political advertisement videos

To convince voters, all kinds of strategies can be used to stimulate the electorate. Researcher Dias and his team evaluated how voters respond to changes in the scenario of videos of political propaganda.
 
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