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


  • Benta, K.-I. ; Van Kuilenburg, H.; Eligio, U.X.; Den Uyl, M.; Cremene, M.; Hoszu, A.; Cret, O. (2009). Evaluation of a system for real-time valence assessment of spontaneous facial expressions. Distributed Environments Adaptability, Semantics and Security Issues, International Romanian – French Workshop, Cluj-Napoca, Romania, September, 17-18, 2009.
  • 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 Unpublished manuscript retrieved from and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.
  • Den Uyl, M.J.; Van Kuilenburg, H. (2008). The FaceReader: Online Facial Expression Recognition. Proceedings of Measuring Behavior 2005, Wageningen, The Netherlands, August 30 - September 2, 2008, pp. 589-590.
  • 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.
  • Van Kuilenburg, H.; Den Uyl, M.J.; Israël, M.L.; Ivan, P. (2008). Advances in face and gesture analysis. Proceedings of Measuring Behavior 2008, Maastricht, The Netherlands, August 26-29, 2008, pp. 371-372.
  • 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. 
  • Crist, C.C.; Duncan, S.E.; Gallagher, D.L. (2016). Application of facial analysis technology and data analysis for assessing emotional response to beverages. J. Visual Exp. e54046, doi:10.3791/54046. Video available at
  • Danner, L.; Haindle, S.; Duerrschmid, K. (2014). Facial expressions and autonomous nervous system responses elicited by tasting different juices. Food Research International, 64, 81-90.
  • Garcia-Burgos, D.; Zamora, M.C. (2013). Facial affective reactions to bitter-tasting foods and body mass index in adults. Appetite, 71, 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. Appetite, 59 (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 Preference, 48 (A), 130-7, doi: 10.1016/j.foodqual.2015.09.003.
  • Leitch, K.A.; Duncan, S.E.; O’Keefe, S.; Rudd, R.; Gallagher, D.L. (2015). Characterizing consumer emotional response to sweeteners using an emotion terminology questionnaire and facial expression analysis. Food Res. Internat. 76: 283-292.
  • 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.
  • Maroulis, A.; Spink, A.J.; Theuws, J.J.M.; Oster, H.; Buitelaar, J. (2017). Sweet or sour. Validating baby-facereader to analyse infant responses to food. Poster presentation at Pangborn 2017.
  • 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 Technology, 69, 161-168.
  • Walsh, A.M.; Duncan, S.E.; Bell, M.A.; O'Keefe, S.F.; Gallagher, D.L. (2017). Integrating implicit and explicit emotional assessment of food quality and safety concerns. Food Quality and Preference, 56, 212-224.
  • Walsh, A.M.; Duncan, S.E.; Bell, M.A.; O'Keefe S.F.; Gallagher, D.L. (2017). Breakfast meals and emotions: Implicit and explicit assessment of the visual experience. Journal of Sensory Studies, doi: 10.1111/joss.122265.
  • Walsh, A.M.; Duncan, S.E.; Potts, H.; Gallagher, D.L. (2015). Comparing quality and emotional responses as related to acceptability of light-induced oxidation flavor in milk. Food Res. Internat. 76 :293-300.
  • 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 Preference, 26 (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.


  • Aguiar, Y.P.C.; Vieira, M.F.Q.; Galy, E.; Mercantini, J.M.; Santoni, C. (2011). Refining a user behaviour model based on the observation of emotional states, Proceedings Cognitive 2011: the third international conference on advanced cognitive technologies and applications, 36-40.
  • Chentsova-Dutton, Y.E.; Tsai, J.L. (2010). Self-focused attention and emotional reactivity: the role of culture. Journal of Personality and Social Psychology98 (3), 507-519.
  • 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 Psychology, 152, 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 Psychology, 6 (1089),
  • Gorbunov, R.; Barakova, E.I. & Rauterberg, M. (2017). Memory effect in expressed emotions during long term group interactions., doi: 10.1007/978-3-319-59740-9_25.
  • 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.
  • Maroulis, A.; Spink, A.J.; Theuws, J.J.M.; Oster, H.; Buitelaar, J. (2017). Sweet or sour. Validating baby-facereader to analyse infant responses to food. Poster presentation at Pangborn 2017.
  • 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.
  • Drape, T.A., Epler, C., Rudd, R., Moore, D.M. (2009). The Moments We Miss: Using Facial Reader Software as an Educational Research Tool. Presentation at Association of Career and Technical Education for Research, Nashville, TN.
  • 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 Behavior, 48, 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 Science, 7926, 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. (2010). Measuring Instant Emotions During a Self-Assessment Test: The Use of FaceReader. Proceedings of Measuring Behavior 2010 (Eindhoven, The Netherlands, August 24-27, 2010) 192-195 
  • 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 Education, 45 (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 Science, 173, 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 Communication, 54, 1049-1063.

User experience research

  • Courgeon, M.; Martin, J.C.; Jacquemin, C. (2008). MARC: a multimodal affective and reactive character, International Workshop on Affective Interaction in Natural Environments (AFFINE) held in cunjunction with the 10th International Conference on Multimodal Interaction (ICMI'2008). Chania - Greece,October 24th.
  • 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 Experiment. Proceedings of Measuring Behavior 2012 (Utrecht, The Netherlands, August 28-31, 2012), 365-367.
  • Grootjen, M.; Neerincx, M.A.; Weert J.C.M. van; Truong, K.P. (2007). Measuring Cognitive Task Load on a Naval Ship: Implications of a Real World Environment. Proceedings ACI/HCII, Beijing, 2007.
  • Melder, W.A. et al (2007). Affective Multimodal Mirror: Sensing and Eliciting Laughter. International Multimedia Conference. Retrieved from:
  • 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 Behavior, 60, 19-28.
  • Truong K.P. (2007). Unobtrusive multimodal emotion detection in adaptive interfaces. Speech and facial expressions. D.D. Schmorrow, L.M. Reeves (Eds.): Augmented Cognition, HCII 2007, LNAI 4565, pp. 354–363, 2007.
  • Zaman, B.; Shrimpton-Smith, T. (2006). The FaceReader: Measuring instant fun of use. NordiCHI, 14-16 October 2006.

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.