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FaceReader can detect facial expressions, and offers a number of extra classifications. Do you want to learn more about how FaceReader works? Read on!
Best practices FaceReader Online
Do you want to know how online facial expression analysis service FaceReader Online can benefit your UX and market research? Read on!
How to build an AV-Lab
An AudioVisual lab is designed to allow you to observe your test participants unobtrusively. Do you want to learn more how to set up an AV-lab? Read on!
FaceReader product videos
FaceReader Classifications Demo
Discover the realtime modeling capabilities of FaceReader.
Affective attitudes in FaceReader
Using the Action Unit Module, Facereader is able to detect the affective attitude 'confusion'.
Baby FaceReader enables you to recognize the facial expressions of an infant automatically!
What does your face say?
Curious what emotions your face shows? Upload a photo here, and our FaceReader software will test it for emotionality. The better quality the picture, the better the results! To get the best results, test only photos where your face is clearly visible. The lighting should be sufficient as well.
Try it now and find out what your face says.
Customer success stories
How to measure emotions in the Uses and Acceptability Lab
Jean-Marc Diverrez, bcom Institute of Research and Technology
Sensory evaluation - Food science & technology
Prof. Susan Duncan, Virginia Tech
Social Media Lab: How people make sense of data
Tiffany Andry, University of Louvain
- Bartkiene, E.; Steibliene, V.: Adomaitiene, V.; Juodeikiene, G.; Cernauskas, D.; Lele, V.; Klupsaite, D.; Zadeike, D.; Jarutiene, L. & Guiné, R.P.F. (2019). Factors Affecting Consumer Food Preferences: Food Taste and Depression-Based Evoked Emotional Expressions with the Use of Face Reading Technology. BioMed Research International, 4, 1-10. https://doi.org/10.1155/2019/2097415.
- De Wijk, R.A.; Kaneko, D.; Dijksterhuis, G.B.; Zoggel, M. van; Schiona, I.; Visalli, M. & Zandstra, E.H. (2019). Food perception and emotion measured over time in-lab and in-home. Food Quality and Preference, 75, 170-178. https://doi.org/10.1016/j.foodqual.2019.02.019.
- Dupré, D.; Krumhuber, E.G.; Küster, D.; McKeown, G.J. (2020). A performance comparison of eight commercially available automatic classifiers for facial affect recognition. PLoS ONE, 15(4):e0231968. https://doi.org/10.1371/journal.pone.0231968.
- El Haj, M.; Antoine, P. & Nandrino, J.L. (2017). Facial expressions triggered by imagining the future. Journal of Integrative Neuroscience, 16(4), 483–492. https://doi.org/10.3233/JIN-170030.
- Meng, Q.; Hu, X.; Kang, J.; Wu, Y. (2020). On the effectiveness of facial expression recognition for evaluation of urban sound perception. Science of The Total Environment, 710, 135484, ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2019.135484.
- Noordewier, M.K. & Dijk, E. van (2018). Surprise: unfolding of facial expressions. Cognition and Emotion. https://doi.org/10.1080/02699931.2018.1517730.
- Riem, M.M.E. & Karreman, A. (2019). Experimental manipulation of emotion regulation changes mothers’ physiological and facial expressive responses to infant crying. Infant Behavior and Development, 55, 22-31. https://doi.org/10.1016/j.infbeh.2019.02.003.
- Stöckli, S.; Schulte-Mecklenbeck, M.; Borer, S. & Samson, A.C. (2018). Facial expression analysis with AFFDEX and FACET: A validation study. Behav Res Methods, 50(4), 1446-1460. https://doi.org/10.3758/s13428-017-0996-1.
- Talen, L. & den Uyl, T.E. (2021). Complex Website Tasks Increase the Expression Anger Measured with FaceReader Online. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2021.1938390.
- Yi, Q.; Xiong, S.; Wang B.; Yi, S. (2020). Identification of trusted interactive behavior based on mouse behavior considering web User's emotions. International Journal of Industrial Ergonomics, 76(102903), https://doi.org/10.1016/j.ergon.2019.102903.
- Zhi, R.; Cao, L.; Cao, G. (2017). Asians' Facial Responsiveness to Basic Tastes by Automated Facial Expression Analysis System. J Food Sci. 82(3), 794-806. doi: 10.1111/1750-3841.13611.