Automatic measurement of ad preferences from facial responses gathered over the Internet
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Methods for recognizing other affective states have been proposed, namely cognitive states like boredom, confusion, delight, concentration and frustration, positive and negative affect from groups of people, or liking/not-liking of online media for predicting buying behaviour for marketing purposes.
“Methods for recognizing other affective states have also been proposed, namely cognitive states like boredom, confusion, delight, concentration and frustration [266], positive and negative affect from groups of people [267] or liking/not-linking of online media for predicting buying behaviour for marketing purposes [268].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [268]
Most approaches target primitive facial expressions of affect, but others recognize cognitive states, positive and negative affect from groups of people, and liking/not-liking of online media for predicting buying behaviour for marketing purposes.
“Most approaches target primitive FEs of affect, but others recognize cognitive states [266], postive and negative affect from groups of people [267] and liking/not-linking of online media for predicting buying behaviour for marketing purposes [268].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [268]
In naturalistic environments, most approaches target primitive facial expressions of affect, but methods for recognizing cognitive states, positive and negative affect from groups of people, or liking/not-liking of online media for predicting buying behaviour for marketing purposes are also common.
“In naturalistic environments, most approaches target primitive FEs of affect. Methods for recognizing cognitive states [266], positive and negative affect from groups of people [267] or liking/not-linking of online media for predicting buying behaviour for marketing purposes [268] are also common.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [268]
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,953 · see citing works
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019 · see citing works
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797 · see citing works
Computer vision and pattern recognition
Zheng, N., Loizou, G. D., Jiang, X., Lan, X., & Li, X. (2007) · International Journal of Computer Mathematics · doi:10.1080/00207160701303912 · Cited by 2,463 · see citing works
Hidden Conditional Random Fields for Gesture Recognition
Wang, S. B., Quattoni, A., Morency, L., Demirdjian, D., & Darrell, T. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.132 · Cited by 456 · see citing works
Self-citationEmotion-Induced Engagement in Internet Video Advertisements
Teixeira, T. S., Wedel, M., & Pieters, R. G. M. (2011) · Journal of Marketing Research · doi:10.1509/jmr.10.0207 · Cited by 439 · see citing works
Bolls, P. D., Lang, A., & Potter, R. F. (2001) · Communication Research · doi:10.1177/009365001028005003 · Cited by 392 · see citing works
Latent-Dynamic Discriminative Models for Continuous Gesture Recognition
Morency, L., Quattoni, A., & Darrell, T. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383299 · Cited by 376 · see citing works
Ambadar, Z., Cohn, J. F., & Reed, L. I. (2008) · Journal of Nonverbal Behavior · doi:10.1007/s10919-008-0059-5 · Cited by 319 · see citing works
Toward Practical Smile Detection
Whitehill, J., Littlewort, G. C., Fasel, I., Bartlett, M. S., & Movellan, J. R. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.42 · Cited by 310 · see citing works
The ARF Copy Research Validity Project
Haley, R. I., & Baldinger, A. L. (2000) · Journal of Advertising Research · doi:10.2501/jar-40-6-114-135 · Cited by 277 · see citing works
McDuff, D., el Kaliouby, R., Senechal, T., Amr, M., Cohn, J. F., & Picard, R. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2013.130 · Cited by 234 · see citing works
Self-citationEmotional Response to Television Commercials: Facial EMG vs. Self-Report
Hazlett, R. L., & Hazlett, S. Y. (1999) · Journal of Advertising Research · doi:10.1080/00218499.1999.12466597 · Cited by 203 · see citing works
How to distinguish posed from spontaneous smiles using geometric features
Valstar, M., Güneş, H., & Pantić, M. (2007) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1322192.1322202 · Cited by 201 · see citing works
Exploring Temporal Patterns in Classifying Frustrated and Delighted Smiles
Hoque, M. E., McDuff, D., & Picard, R. W. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.11 · Cited by 164 · see citing works
Self-citationMoment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing
Teixeira, T. S., Wedel, M., & Pieters, R. G. M. (2010) · Marketing Science · doi:10.1287/mksc.1100.0567 · Cited by 164 · see citing works
Crowdsourcing Facial Responses to Online Videos
McDuff, D., el Kaliouby, R., & Picard, R. W. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.19 · Cited by 124 · see citing works
Self-citationMeasurable Emotions: How Television Ads Really Work
Micu, A. C., & Plummer, J. T. (2010) · Journal of Advertising Research · doi:10.2501/s0021849910091300 · Cited by 123 · see citing works
Effects of Advertising Likeability: A 10-Year Perspective
Smit, E. G., van Meurs, L., & Neijens, P. C. (2006) · Journal of Advertising Research · doi:10.2501/s0021849906060089 · Cited by 113 · see citing works
Joho, H., Staiano, J., Sebe, N., & Jose, J. (2010) · Multimedia tools and applications · doi:10.1007/s11042-010-0632-x · Cited by 104 · see citing works
Smile detection by boosting pixel differences
Shan, C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2161587 · Cited by 90 · see citing works
Predicting online media effectiveness based on smile responses gathered over the Internet
McDuff, D., el Kaliouby, R., Demirdjian, D., & Picard, R. W. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553750 · Cited by 70 · see citing works
Self-citationAffect valence inference from facial action unit spectrograms
McDuff, D., el Kaliouby, R., Kassam, K., & Picard, R. W. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543833 · Cited by 53 · see citing works
Self-citationVideo classification and recommendation based on affective analysis of viewers
Zhao, S., Yao, H., & Sun, X. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.04.042 · Cited by 47 · see citing works
Multi-signal gesture recognition using temporal smoothing hidden conditional random fields
Song, Y., Demirdjian, D., & Davis, R. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771431 · Cited by 32 · see citing works
Self-citationCrowdsourced data collection of facial responses
McDuff, D., el Kaliouby, R., & Picard, R. W. (2011) · International Conference on Multimodal Interfaces · doi:10.1145/2070481.2070486 · Cited by 27 · see citing works
Self-citationRegistration Invariant Representations for Expression Detection
Lucey, P., Lucey, S., & Cohn, J. F. (2010) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2010.53 · Cited by 18 · see citing works
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3 of the 35 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Corneanu, C. A., Simon, M. O., Cohn, J. F., & Guerrero, S. E. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2515606 · Cited by 527 · see citing works
“negative affect fromgroups of people [268] or liking/not-linking of online media for predicting buying behaviour for marketing purposes [269] are also common.”
BackgroundAutomatic Detection of Learning-Centered Affective States in the Wild
Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701397 · Cited by 178 · see citing works
“This dataset has been used for detection of whether viewers liked the commercials (AUC = .82) and wanted to view them again (AUC = .79) [32].”
MethodEmotion recognition in the wild from videos using images
Bargal, S. A., Barsoum, E., Ferrer, C. C., & Zhang, C. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2997627 · Cited by 173 · see citing works
“Emotion recognition from video has also been revolutionizing marketing for quantifying ad preferences [17].”
BackgroundPredicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads
McDuff, D., el Kaliouby, R., Cohn, J. F., & Picard, R. W. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2384198 · Cited by 165 · see citing works
Self-citationAdvances, Challenges, and Opportunities in Automatic Facial Expression Recognition
Martínez, B., & Valstar, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-25958-1_4 · Cited by 136 · see citing works
“the automatic analysis of marketing and publicity [84, 85].”
BackgroundUsing Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms
Bosch, N., D'Mello, S. K., Ocumpaugh, J. L., Baker, R., & Shute, V. (2016) · TIIS · doi:10.1145/2946837 · Cited by 124 · see citing works
“The authors were also able to detect whether viewers liked the commercials (AUC = .82) and wanted to view them again (AUC = .79) [McDuff et al. 2014].”
BackgroundFacial Action Unit Detection Using Active Learning and an Efficient Non-linear Kernel Approximation
Sénéchal, T., McDuff, D., & el Kaliouby, R. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.11 · Cited by 59 · see citing works
Self-citationThe spectrum of facial palsy: The MEEI facial palsy photo and video standard set
Greene, J. J., Guarín, D. L., Tavares, J. D. P., Fortier, E., Robinson, M. W., Dusseldorp, J. R., Quatela, O. E., Jowett, N., & Hadlock, T. A. (2019) · The Laryngoscope · doi:10.1002/lary.27986 · Cited by 58 · see citing works
In the Eye of the Beholder: Changes in Perceived Emotion Expression after Smile Reanimation
Dusseldorp, J. R., Guarín, D. L., van Veen, M. M., Jowett, N., & Hadlock, T. A. (2019) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000005865 · Cited by 55 · see citing works
Emotion recognition in the wild using deep neural networks and Bayesian classifiers
Surace, L., Patacchiola, M., Sönmez, E. B., Spataro, W., & Cangelosi, A. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3143015 · Cited by 55 · see citing works
“Automatic emotion recognition has recently become an important research field, due to new possible applications in social media, marketing [24], public safety [5], and human-computer interaction [7].”
BackgroundDeep neural network-based fusion model for emotion recognition using visual data
Do, L.-N., Yang, H.-J., Nguyen, H.-D., Kim, S.-H., Lee, G.-S., & Na, I. S. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03690-y · Cited by 50 · see citing works
Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain
Aung, M. H., Alquaddoomi, F., Hsieh, C.-K., Rabbi, M., Yang, L., Pollak, J., Estrin, D. L., & Choudhury, T. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2565381 · Cited by 38 · see citing works
Pham, P., & Wang, J. (2017) · International Conference on Intelligent User Interfaces · doi:10.1145/3025171.3025186 · Cited by 31 · see citing works
“A similar approach has been done by McDuff et al. [29], where the authors only reported performance on a dataset having neutral and mild (weak) responses to ads removed.”
BackgroundMethodInfluentialHöfling, T. T. A., & Alpers, G. W. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1125983 · Cited by 30 · see citing works
Exemplar Hidden Markov Models for classification of facial expressions in videos
Sikka, K., Dhall, A., & Bartlett, M. S. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301350 · Cited by 26 · see citing works
“Similar to the protocol used in [13], only videos where the labels are either positive or negative were included and the target was to predict these binary selfreport responses given a test video.”
MethodTeal, R., Roberts, M., Harrigan, P., Clarkson, J., & Rosenberg, M. (2019) · Sport Management Review · doi:10.1016/j.smr.2019.05.003 · Cited by 21 · see citing works
Event detection: Ultra large-scale clustering of facial expressions
Vandal, T., McDuff, D., & el Kaliouby, R. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163079 · Cited by 19 · see citing works
“Furthermore, we can make inferences from the observed behaviors, such as viewer preferences for online videos [4] and purchase intent towards advertised brands [5].”
BackgroundSelf-citationFacial expression recognition through adaptive learning of local motion descriptor
Agarwal, S., & Mukherjee, D. P. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3103-6 · Cited by 18 · see citing works
Crowdsourcing facial responses to online videos: Extended abstract
McDuff, D., el Kaliouby, R., & Picard, R. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344618 · Cited by 17 · see citing works
“The diversity and size of the training set have led to considerable improvements in the classifier performance [17], [18].”
BackgroundSelf-citationBezawada, S., Hu, Q., Gray, A., Brick, T. R., & Tucker, C. S. (2017) · Journal of Mechanical Design · doi:10.1115/1.4035428 · Cited by 17 · see citing works
“Other observational methods capture the psychological experience of the designer because emotional states relate to productivity of the designer’s process [38].”
BackgroundAn Emerging Decision Authority
Töniges, T., Ötting, S. K., Wrede, B., Maier, G. W., & Sagerer, G. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-803801-7.00026-2 · Cited by 14 · see citing works
Cluley, R. (2021) · Marketing Theory · doi:10.1177/14705931211039001 · Cited by 14 · see citing works
“McDuff et al. (2014: 638) observe ‘a large number of the false positive and false negatives occur when the viewers are relatively inexpres-sive’.”
BackgroundZapping Index:Using Smile to Measure Advertisement Zapping Likelihood
Yang, S., Kafai, M., An, L., & Bhanu, B. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2364581 · Cited by 14 · see citing works
Supporting the Design of Technology-Mediated Sharing Practices
Fedosov, A. (2020) · Carl Grossmann eBooks · doi:10.24921/2020.94115943 · Cited by 13 · see citing works
Tzafilkou, K., Economides, A. A., & Panavou, F. R. (2023) · Computers · doi:10.3390/computers12040088 · Cited by 10 · see citing works
Can muted video advertising be as effective as video advertising with sound?
Bellman, S., Arismendez, S. V., & Varan, D. (2021) · SN Business & Economics · doi:10.1007/s43546-020-00030-9 · Cited by 9 · see citing works
Cascade of Tasks for facial expression analysis
Ding, X., Chu, W.-S., de la Torre, F. N., Cohn, J. F., & Wang, Q. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.03.008 · Cited by 9 · see citing works
“Existing work typically poses AU detection as a classification problem between frames or segments of positive and negative examples, and emphasizes the use of different features or classifiers.”
BackgroundPham, P. T., & Wang, J. (2019) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3232233 · Cited by 9 · see citing works
“[28, 29] explored the use of commodity webcams and FEA to analyze viewers’ emotional responses and purchase intent from video advertisements on PCs via Adobe Flash widgets embedded in web pages.”
BackgroundMethodInfluentialNeuroscience Applications on the Assessments of TV Ads
Çakar, T. E., & Gez, K. (2016) · Advances in business strategy and competitive advantage book series · doi:10.4018/978-1-5225-1028-4.ch010 · Cited by 8 · see citing works
Smile intensity recognition in real time videos: fuzzy system approach
Vinola, C., & Devi, K. V. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6890-8 · Cited by 7 · see citing works
“In [21], with the aim of using smile detector response in measuring the ad preferences of the AMFED dataset, two smile detector algorithms(v1 and v2) are developed based on appearance features with Area Under the Curve (Receiver Operating Characteristic Curve) as 0.”
BackgroundMethodInvestigating Taste-liking with a Humanoid Robot Facilitator
Jie, Z., & Güneş, H. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223611 · Cited by 6 · see citing works
“These have limitations caused by time and ability to give feedback [1], difficulty in quantifying rating metrics [3], and inherent bias due to people’s conscious and rational processing in answering the questions [4].”
BackgroundEmotional and Social Signals: A Neglected Frontier in Multimedia Computing?
Güneş, H., & Hung, H. (2015) · IEEE Multimedia · doi:10.1109/mmul.2015.37 · Cited by 6 · see citing works
Li, T., Du, C., Naren, T., Chen, Z., Liu, S., Zhou, J., & Xu, X. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.0009 · Cited by 5 · see citing works
Group Emotion Recognition Using Machine LearningPreprint
Garg, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01118 · Cited by 5 · see citing works
“Automatic emotion recognition is a challenging task that has gained significant scientific interest in recent years due to its applications in crowd analytics, social media, marketing [2], event detection and summarization, public safety [3], humancomputer interaction [4], digital security surveillance, street analyti…”
BackgroundEstimation of affective dimensions using CNN-based features of audiovisual data
Basnet, R., Islam, M. T., Howlader, T., Rahman, S. M. M., & Hatzinakos, D. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.09.015 · Cited by 4 · see citing works
Fedosov, A., Stancu, B., Lascio, E. D., Eynard, D., & Langheinrich, M. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3313261 · Cited by 4 · see citing works
“In general, understanding emotions of video clips (e.g., advertisements on TV) is seen as a key challenge in marketing research efforts such as to determine likability of ads (e.g., [6.]).”
BackgroundKremsner, T. P., Pfeiffer, C., Weidinger, S., & Stolavetz, C. (2023) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2023.100202 · Cited by 4 · see citing works
AttentiveVideo: quantifying emotional responses to mobile video advertisements
Pham, P. T., & Wang, J. (2016) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2993148.2998533 · Cited by 3 · see citing works
“Although autonomic feedback channels such as facial expressions [4] and physiological signals [3] have been used as supplemental channels for the quality of advertisements, these approaches require either dedicated sensors [3] or PCs connected to high speed internet [4].”
MethodNeuroscience Applications on the Assessments of TV Ads
Çakar, T. E., & Gez, K. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5478-3.ch018 · Cited by 3 · see citing works
Implicit Media Tagging and Affect Prediction from RGB-D Video of Spontaneous Facial Expressions
Hadar, D., Tron, T., & Weinshall, D. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.91 · Cited by 2 · see citing works
“[11] who used a narrow set of facial features (smiles and their dynamics) to predict a participant’s likability response and desire to watch again.”
BackgroundTwosome Modelling based Emotion Recognition in Videos
Kasraoui, S., Lachiri, Z., & Madani, K. (2019) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2019.8924297 · Cited by 2 · see citing works
Identifying Symptoms Using Technology
Doryab, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-69638-6_11 · Cited by 2 · see citing works
Prediction of instantaneous likeability of advertisements using deep learning
Saha, D., Rahman, S. M. M., Islam, M. T., Ahmad, M. O., & Swamy, M. N. S. (2021) · Cognitive Computation and Systems · doi:10.1049/ccs2.12022 · Cited by 2 · see citing works
“Over this time span, researchers from related fields have reached a consensus that the success and likeability of an advertisement are highly correlated [2, 3].”
BackgroundAutomatic Analysis of Facilitated Taste-liking
Chen, Y., Jie, Z., & Güneş, H. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425645 · Cited by 1 · see citing works
“[28], and inherent bias due to people’s conscious and rational pro-”
BackgroundBiometric Identification in eHealthcare: Learning from the Cases of Russia and Italy
Kachurina, P., Buccafurri, F., Bershadskaya, L., Bershadskaya, E., & Trutnev, D. R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22389-6_8
Recognizing Facial Expressions in the Orthogonal Complement of Principal Subspace
Agarwal, S., Mazumder, S., & Mukherjee, D. P. (2014) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2683483.2683560
“In recent times emotion and sentiment analysis have been used for marketing and advertisements [17].”
MethodImproving and Scaling Mobile Learning via Emotion and Cognitive-state Aware Interfaces
Pham, P. T. (2018) · D-Scholarship@Pitt (University of Pittsburgh)
de Almeida, N. C., & Lucena, T. F. R. (2019) · Paradoxos · doi:10.14393/par-v3n1-2018-49692
Lin, J., Liu, Z., Hung, C., Liu, T., & Chai, Y. (2020)
Facial Action Unit Recognition Using Pseudo-Intensities and their Transformation
Saito, J., Yamamoto, T., Uchida, A., Mi, X., & Murase, K. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9666995
Uncertainty Prediction for Facial Action Units Recognition under Degraded Conditions
Saito, J., Youoku, S., Kawamura, R., Uchida, A., Murase, K., & Mi, X. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00069
“There are many applications, including interest analysis in marketing [2],mental health analysis in healthcare [3], and student engagement analysis in education [4].”
BackgroundBekenova, G., & Orazgaliyeva, E. B. (2026) · BUKETOV BUSINESS REVIEW · doi:10.31489/2026ec1/1248
“McDuff et al. (2014) extended this insight to facial expressions, demonstrating that temporal dynamics of smiles—not just their presence—correlated with ad liking and memory (McDuff et al., 2014).”
- Authors
- McDuff, D., El Kaliouby, R., Senechal, T., Demirdjian, D., & Picard, R.
- Affiliations
- MIT Media Lab (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- McDuff, Daniel
- Demirdjian, David
- Picard, Rosalind
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 32
- Issue
- 10
- Pages
- 630-640
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2047967129
- Cited by
- 52
- References
- 35
- Citations by year
- 2026: 1 · 2023: 3 · 2022: 1 · 2021: 5 · 2020: 4 · 2019: 9 · 2018: 5 · 2017: 3 · 2016: 10 · 2015: 8 · 2014: 3
- FWCI
- 11.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Image and Vision Computing (publishedVersion)
- Funders
- Media Lab Consortium
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- online video advertising, ad liking, facial expression analysis, smile detection, advertising effectiveness, copy testing
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