Noldus Face Reader
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Noldus Facereader 7
FaceReader Online is the user-friendly online facial expression analysis. Well-visible face). Which is an initiative of VicarVision and Noldus Information.
Noldus Facereader
FaceReader is used worldwide at more than 600 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. Here are some examples:.
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?
Selected publications This is a selection of publications that mention the use of FaceReader. If you feel your publication should be on this list, please let us know. Methodology. Bijlstra, G., & Dotsch, R. FaceReader 4 emotion classification performance on images from the Radboud Faces Database. Unpublished manuscript retrieved from and.
Cohen, A.S.; Morrison, S.; Callaway. Computerized facial analysis for understanding constricted/blunted affect: initial feasibility, reliability, and validity data. Schizophrenia Research. L., & Liaw, H. The role of facial microexpression state (FMES) change in the process of conceptual conflict. British Journal of Educational Technology. Danner, L.; Sidorkina, L.; Joechl, M.; Duerrschmid.
(2014) Make a face! Implicit and explicit measurement of facial expressions elicited by orange juices using face reading technology.
Food Quality and Preference, doi:10.1016/j.foodqual.2013.01.004. Danner, L.; Sidorkina, L.; Duerrschmid, K. Implicit and explicit measurement of facial reactions elicited by model foods using FaceReading Technology. Proceedings of the 5th European Conference on Sensory and Consumer Research, P9-15. D'Arcey, T.; Johnson, M.; Ennis, M. Proceedings of APS 24th annual meeting.
D'Arcey, J.T.; Johnson, M.R.; Ennis, M.; Sanders, P.; Shapiro, M.S. FaceReader's assessment of happy and angry expressions predicts zygomaticus and corrugator muscle activity.
Poster presentation VIII-014 Association for Psychological Science meeting, 23-26 May 2013. Gudi, A.; Tasli, H.E.; den Uyl T. & Maroulis, A. (2015).
Deep Learning based FACS Action Unit Occurrence and Intensity Estimation. Automatic Faces and Gesture Recognition (FG), doi:.
Halasz, J., Aspan, N., Vida, P. Output properties for validated static inputs in a facial affect recognition system. AIS.
Lewinski, P.; den Uyl, T.M.; Butler, C. Journal of Neuroscience, Psychology, and Economics, 7(4), 227-236.
Loijens, L.W.S.; Theuws, J.M.M.; Spink, A.J.; Ivan, P.; Den Uyl, M. Analyzing facial expressions with FaceReader: Evaluation of improvements to the software for exploring consumer behavior. Proceedings of the 5th European Conference on Sensory and Consumer Research, P 8.8. Van Kuilenburg, H.; Wiering, M; Den Uyl, M.J.
A Model Based Method for Automatic Facial Expression Recognition. Proceedings of the 16th European Conference on Machine Learning, Porto, Portugal, 2005, pp. 194-205, Springer-Verlag GmbH. Shahid, S.; Krahmer, E.; Neerincx, M.; Swerts, M. Positive Affective Interactions: The Role of Repeated Exposure and Co-Presence.
IEEE Transactions on Journal Computing. Sideridis, G. D., Kaplan, A., Papadopoulos, C., & Anastasiadis, V. The affective experience of normative-performance and outcome goal pursuit: Physiological, observed, and self-report indicators. Learning and Individual Differences.
Further ‘in silico’ validation of a facial affect recognition system.