To gain accurate and reliable data about facial expressions, FaceReader is the most robust automated system that will help you out.
Why use FaceReader
- Automated analysis of facial expressions brings 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
Continue your research during COVID-19 pandemic
Due to the COVID-19 pandemic, many people are currently facing lab closures and have to work from home, trying to keep research projects going. Noldus wants to make the transfer to the home office of our customers as easy as possible, so that they can continue their facial expression analysis work remotely. Here's what we can do right now:
- If your lab has a site license of FaceReader, you can access it via the IP address and port number provided to you by your IT department.
- It might be that you just weren’t able to collect your key before your lab was closed. For this purpose, Noldus Information Technology makes 1,000 remote-access licenses of FaceReader available.
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
- Finds a face with the popular Viola-Jones algorithm
- Makes an accurate 3D face modeling using > 500 key points (mesh) as well as the texture of the face
- Uses the AI technique Deep Learning to analyze the face, even if a part of it is hidden
- 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.
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% . FaceReader 7.1 achieves an even higher score, with 93% .
Trusted by researchers around the world
Who uses it?
FaceReader is used worldwide at more than 1000 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.