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

 

Efficient coding with FaceReader

At the Social Behavior Lab at Western University, dr. Erin Heerey explores human behavior during social interactions. 

Frame-by-frame expression analysis of her project would have taken 800 hours of manual coding. FaceReader did it in only 14 hours! This has not only increased throughput, accuracy, and ease of scoring social behaviors, but has also made it much easier to share data with researchers.

 



FaceReader benefits your work

Many researchers have turned towards using automated facial expression analysis software to better provide an objective assessment of emotions. FaceReader is used worldwide at more than 1,000 universities, research institutes, and companies in many markets, from consumer and psychology research to usability studies. 

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.

 
 


Measure emotions with FaceReader

FaceReader is the best automated system for the recognition of specific properties in facial images and expressions. Aside from the basic or universal facial expressions, you can define your own Custom Expressions. Additionally, FaceReader can recognize a neutral state and analyze contempt.

Whether your test participant is a baby, child, adult, or 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 face-finding algorithm based on Deep Learning
  2. Face modeling – Makes an accurate artificial face model using almost 500 key points
  3. Face classification – Classifies facial expressions with artificial neural networks

As a result, you'll have data on basic and 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 the ADFES data set, FaceReader 9 delivers accurate performance for emotion classification, with an average accuracy of 99% [1].

Facereader 9 Average Performance

Accuracy rates for the 6 basic expressions in FaceReader

References

  1. The average performance of FaceReader 9 was measured with the 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. Professionals are active in many markets, including psychology, consumer research, user experience, human factors, and neuromarketing.

Are you next?

 

 


Relevant blogs

How do people with antisocial and psychopathic traits process emotions?

How do people with antisocial and psychopathic traits process emotions?

Understanding more about emotion processing in people with antisocial personality disorder and psychopathic traits can improve interventions. The team of researcher Kyranides studied how facial mimicry can help.
Measuring consumer responses to chocolate and images

Measuring consumer responses to chocolate and images

Researchers all over the world are trying to find ways to measure real consumer responses and behavior.
Analysis of facial expressions of emotions in children

Analysis of facial expressions of emotions in children

The study described in this guest blog post focuses on the facial expressions of emotions induced by affective stimuli in children aged between 7 and 14.
 
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