Users of 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?

Client list

Please download the list for an overview of our customers per the below mentioned markets, or contact us directly for more information.

  • Psychology
  • Consumer behavior
  • Human factors
  • Neuromarketing
  • Healthcare

Blog posts about the use of FaceReader

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. On our Behavioral Research Blog, you can find all these different studies, and more, to inform you about the added value of FaceReader.

Are you interested in writing a blog post yourself about your research, using FaceReader? Please contact us to discuss the details.

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 at


    • Bijlstra, G., & Dotsch, R. (2011). FaceReader 4 emotion classification performance on images from the Radboud Faces Database. Unpublished manuscript retrieved from and
    • Cohen, A.S.; Morrison, S.; Callaway. D.A. (2013). Computerized facial analysis for understanding constricted/blunted affect: initial feasibility, reliability, and validity data. Schizophrenia Research.
    • Chiu, M. H., Chou, C. C., Wu, W. L., & Liaw, H. (2014). 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. (2012). Make a face! 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. (2012). Assessing the validity of FaceReader using facial electromyography. Proceedings of APS 24th annual meeting.
    • D'Arcey, J.T.; Johnson, M.R.; Ennis, M.; Sanders, P.; Shapiro, M.S. (2013). 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. M. & Maroulis, A. (2015). Deep Learning based FACS Action Unit Occurrence and Intensity Estimation. Automatic Faces and Gesture Recognition (FG), doi: 10.1109/FG.2015.7284873.
    • Halasz, J., Aspan, N., Vida, P. & Balazs, J. (2014). Output properties for validated static inputs in a facial affect recognition system. AIS
    • Lewinski, P.; den Uyl, T.M.; Butler, C. (2014). Automated facial coding: validation of basic emotions and facs aus in FaceReader. 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. (2012). 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. (2005). 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. (2012). 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. (2014). The affective experience of normative-performance and outcome goal pursuit: Physiological, observed, and self-report indicators. Learning and Individual Differences.
    • Vida, P. & Halasz, J. (2015). Further ‘in silico’ validation of a facial affect recognition system. AIS

Consumer behavior research & marketing studies

  • Chavaglia Neto, J.; Filipe, J.A. (2015). Consumers economic behavior and emotions: the case of iphone 6 in neuromarketing. International Journal of Latest Trends in Finance & Economic Sciences, Vol-5, No.4. 
  • Danner, L.; Haindle, S.; Duerrschmid, K. (2014). Facial expressions and autonomous nervous system responses elicited by tasting different juices. Food Research International64, 81-90.
  • Garcia-Burgos, D.; Zamora, M.C. (2013). Facial affective reactions to bitter-tasting foods and body mass index in adults. Appetite71, 178-186.
  • He, W.; Boesveldt, S.; Graaf, K. de; Wijk, R.A. de (2012). The effect of positive and negative food odours on human behavioural and physiological responses. Proceedings of the 5th European Conference on Sensory and Consumer Research, OP-1.
  • He, W.; Boesveldt, S.; Graaf, C. de; Wijk, R.A. de (2012). Behavioural and physiological responses to two food odours. Appetite59 (2), 628.
  • He, W.; Boesveldt, S.; Graaf, de, C.; Wijk, R.A. de (2014). Dynamics of autonomic nervous system responses and facial expressions to odors. Frontiers in Psychology, doi: 10.3389/fpsyg.2014.00110.
  • He, W.; Boesveldt, S.; Graaf, C. de; Wijk, R.A. de (2015). The relation between continuous and discrete emotional responses to food odors with facial expressions and non-verbal reports. Food Quality and Preference48 (A), 130-7, doi: 10.1016/j.foodqual.2015.09.003.
  • Lewinski, P. (2015). Don’t look blank, happy, or sad: Patterns of facial expressions of speakers in banks’ YouTube videos predict video’s popularity over time. Journal of Neuroscience, Psychology, and Economics, 8(4), 241-249.
  • Lewinski, P.; Fransen, M. L.; Tan, E.S.H. (2014). Predicting advertising effectiveness by facial expressions in response to amusing persuasive stimuli. Journal of Neuroscience, Psychology, and Economics,  doi: 10.1037/npe0000012.
  • Lewinski, P.; Tan, E.S.; Fransen, M.L.; Czarna, K.; Butler, C. (2014). Hindering facial mimicry in ad viewing: effects on consumers' emotions, attitudes and purchase intentions. ICORIA 2014, Amsterdam.
  • Lewinski, P.; Fransen, M.L.; Tan, E.S.; Snijdewind, M.C.; Weeda, W.D.; Czarna, K. (2014). Do(n't) laugh at that ad: emotion regulation predicts consumers' liking. ICORIA 2014, Amsterdam.
  • Mozuriene, E.; Bartkiene, E.; Juodeikiene, G.; Zadeike, D.; Basinskiene, L.; Maruska, A.; Stankevicius, M.; Ragazinskiene, O.; Damasius, J.; Cizeikiene, D. (2016). The effect of savoury plants, fermented with lactic acid bacterias, on the microbiological contamination, quality, and acceptability of unripened curd cheese. LWT - Food Science and Technology69, 161-168.
  • Wijk, de, R.A.; Kooijman, V.; Verhoeven, R.; Holthuyzen, N.; Graaf, de, C. (2012). Autonomic nervous system responses on and facial expressions of the sight, smell, and taste of liked and disliked foods. Food Quality and Preference26 (2), 196-203.
  • Wijk, de, R. A., He, W., Mensink, M. G., Verhoeven, R. H., & de Graaf, C. (2014). ANS Responses and Facial Expressions Differentiate between the Taste of Commercial Breakfast Drinks. PloS one9 (4), e93823.


  • Choliz, M.; Fernandez-Abascal, E.G. (2012). Recognition of emotional facial expressions: the role of facial and contextual information in the accuracy of recognition, Psychological reports, 110 (1), 338-350.
  • Dys, S.P.; Malti, T. (2016). It's a two-way street: Automatic and controlled processes in children's emotional responses to moral transgressions. Journal of Experimental Child Psychology152, 31-40. doi: 10.1016/j.jecp.2016.06.011
  • Gardia, M.; Aliño, M.; Espert, R.; Salvador, A. (2015). Deceit and facial expression in children: the enabling role of the “poker face” child and the dependent personality of the detector. Frontiers in Psychology6 (1089),
  • Jackson, P.L.; Michon, P-M.; Geslin, E.; Carignan, M.; Beaudoin, D. (2015). EEVEE: the empathy-enhancing virtual evolving environment. Frontiers in Human Neuroscience, doi:10.3389/fnhum.2015.00112.
  • Miyazaki, M., Sugahara, T., Orihara, N. & Umezawa, S. (2017). Footprint of Emotions that Remain in Facial Features: The influence of emotion and facial expression is given to the complexion. 4th International Conference on Computational Science/ Intelligence & Applied Informatics. doi:10.1109/ACIT-CSII-BCD.2017.54.
  • Vida, P., Áspán, N., Szentiványi, D., Horváth, L.O., Keresztény, A., Miklósi, M., Balázs, J. & Halász, J. (2015). Facial emotion expression during a facial emotion recognition task in a clinical sample of adolescents with peer problems. PhD Scientific Meeting 2015 – Semmelweis University.

Educational research

  • Drape, T.A.; Westfall-Rudd, D.; Doak, S.; Guthrie, J.; Mykerezi, P. (2013). Technology Integration in an Agriculture Associate's Degree Program: A Case Study Guided by Rogers' Diffusion of Innovation, NACTA Journal, 24-35.
  • Harley, J.M., Bouchet, F., & Azevedo, R. (2012). Measuring learners’ co-occurring emotional responses during their interaction with a pedagogical agent in MetaTutor. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Lecture Notes in Computer Science: Vol: 7315. Intelligent Tutoring Systems (pp. 40-45). Berlin, Heidelberg: Springer-Verlag.
  • Harley, J.M.; Bouchet, F.; Sazzad Hussain, M.; Azevedo, R.; Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent mulit-agent system. Computers in Human Behavior48, 615-625.
  • Harley, J.M.; Bouchet, F.; Azevedo, R. (2013). Aligning and comparing data on emotions experienced during learning with metatutor. Artificial Intelligence in Education Lecture Notes in Computer Science7926, 61-70.
  • Moridis C.N.; Economides, A.A. (2012). Affective Learning: Empathetic Agents with Emotional Facial and Tone of Voice Expressions. IEEE Transactions of Affective Computing, 3, 260-272.
  • Terzis,V.; Moridis, C. N.; & Economides, A.A. (2012). The effect of emotional feedback on behavioral intention to use computer based assessment. Computers & Education59 (2), 710-721.
  • Seung Woo Choi; Dong Hoon Shin (2017). Development of calculating formula for elementary school students’ scientific positive emotions through FaceReader - Focused on Life Science Videos. Biology Education45 (2), 226-234.

Gaming research

  • Bernhaupt, R.; Boldt, A.; Mirlacher, T.; Wilfinger, D.; Tscheligi, M. (2007). Using emotion in games: emotional flowers, ACE 2007: Proceedings of the international conference on Advances in computer entertainment technology, 41-48. 
  • Chu, K.; Wong, C.Y.; Khong, C.W. (2011). Methodologies for evaluating player experience in game play. HCI International 2011 – Posters’ Extended Abstracts Communications in Computer and Information Science173, Part II, 118-122.
  • Shahid, S.; Krahmer, E.; Swerts, M. (2010). GamE Paradigm: Affective gaming for affect elicitation. ACE 2010, Taipei, Taiwan, 17 November 2010.
  • Truong, K.P.; Leeuwen, van, D.A.; Jong, de, F.M.G. (2012). Speech-based recognition of self-reported and observed emotion in a dimensional space. Speech Communication54, 1049-1063.

User experience research

  • Goldberg, J.H. (2012). Relating perceived web page complexity to emotional valence and eye movement metrics. Proceedings of the Human Factors and Ergonomics Society 56th Annual Meeting, 501-505.
  • Goldberg, J. H. (2014). Measuring Software Screen Complexity: Relating Eye Tracking, Emotional Valence, and Subjective Ratings. International Journal of Human-Computer Interaction. doi:10.1080/10447318.2014.906156.
  • Gorbunov, R.D.; Barakova, E.I.; Ahn, R.M.C.; Rauterberg, G.W.M. (2012) Monitoring Facial Expressions During the Mars-500 Isolation ExperimentProceedings of Measuring Behavior 2012 (Utrecht, The Netherlands, August 28-31, 2012), 365-367.
  • Smets, N.J.J.M; Neerincx, M.A.; Looije, R. (2012) Measuring user behavior in a complex USAR team evaluation Proceedings of Measuring Behavior 2012 (Utrecht, The Netherlands, August 28-31, 2012), 328-331.
  • Staiano, J.; Menendez, M.; Battocchi, A.; De Angeli, A.; Sebe, N. (2012). UX_Mate: from facial expressions to UX evaluation. Proceedings of the Designing Interactive Systems Conference, 741-750.
  • Tay, B.T.C.; Low, S.C.; Ko, K.H.; Park, T. (2016). Types of humor that robots can play. Computers in Human Behavior60, 19-28.

Pain research

  • Boerner, K.E.; Chambers, C.T.; McGrath, P.J.; LoLordo, V.; Uher, R. (2017). The impact of parental modeling on child pain responses: The role of parent and child sex. Journal of Pain, Doi: 10.1016/j.jpain.2017.01.007.
  • Gallant, N.L.; Hadjistavropoulos, T. (2016). Experiencing pain in the presence of others: A structured experimental investigation of older adults. Journal of Pain, doi: 10.1016/j.jpain.2016.12.009.