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Emotion analysis
FaceReader
To gain accurate and reliable data about facial expressions, FaceReader is the most robust automated system that will help you out.
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Clear insights into the effect of different stimuli on emotions
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Very easy to use: save valuable time and resources
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Easy integration with eye tracking data and physiology data
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.
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.
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.
What do I need to run the latest software?
The latest version of FaceReader has been tested on computers with the following technical specifications:
- Windows 11 Pro (64-bit)
- Intel Core i7-10700 (8 Core, 16MB Cache, 2.09GHz)
- 8 GB of internal memory (2X4GB) DDR4 2666 MHz
- NVIDIA Quadro P2200 graphics card, 5G
How FaceReader works
- Face finding – Finds a face using a face-finding algorithm based on Deep Learning
- Face modeling – Makes an accurate artificial face model using almost 500 key points
- 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.
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].
Relevant blogs
Do your emotions and moods change as you get older?
Are emotions affected differently for younger than for older people? Researchers used FaceReader to complement self-assessments and objectify mood changes to answer this question.
Entrepreneurs’ facial expressions & emotional contagion during investor pitches
What is the influence of emotions in a decision-making process? Dinkla and Liebregts wondered if displaying joy and emotional mimicry play a role during pitches.