Automatic coding of facial expressions displayed during posed and genuine pain

Robust Real-Time Face Detection

Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097

Comprehensive database for facial expression analysis

Takeo Kanade, A., Cohn, J. F., & Tian, Y. (2002) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2000.840611 · Cited by 2,644

What the Face RevealsBasic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)

Ekman, P., & Rosenberg, E. L. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195179644.001.0001 · Cited by 1,971

Accuracy of Deception Judgments

Bond, C. F., & DePaulo, B. M. (2006) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr1003_2 · Cited by 1,953

Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage

Ekman, P. (1985) · Cited by 1,716

Web-Based Database for Facial Expression Analysis

Pantić, M., Valstar, M., Rademaker, R. M., & Maat, L. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2005.1521424 · Cited by 1,124

Classifying facial actions

Donato, G., Bartlett, M. S., Hager, J. C., Ekman, P., & B, T. J. S. A. (1999) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.799905 · Cited by 999

The neuropsychology of facial expression: A review of the neurological and psychological mechanisms for producing facial expressions.

Rinn, W. E. (1984) · Psychological Bulletin · doi:10.1037/0033-2909.95.1.52 · Cited by 804

Behavioral markers and recognizability of the smile of enjoyment.

Frank, M. G., Ekman, P., & Friesen, W. V. (1993) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.64.1.83 · Cited by 555

The neuropsychology of facial expression: A review of the neurological and psychological mechanisms for producing facial expressions.

Rinn, W. E. (1984) · Psychological Bulletin · doi:10.1037//0033-2909.95.1.52 · Cited by 538

Automated facial affect analysis for one-on-one tutoring applications

Butko, N. J., Theocharous, G., Philipose, M., & Movellan, J. R. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771430 · Cited by 18

Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals

Amirian, M., Kächele, M., & Schwenker, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46182-3_23 · Cited by 17

Pain expression assessment based on a locality and identity aware network

Xin, X., Li, X., Yang, S.-F., Lin, X., & Zheng, X. (2021) · IET Image Processing · doi:10.1049/ipr2.12282 · Cited by 16

Pain E-motion Faces Database (PEMF): Pain-related micro-clips for emotion research

Fernandes-Magalhaes, R., Carpio, A., Ferrera, D., Ryckeghem, D. M. L. V., Peláez, I., Barjola, P., Lahoz, M. E. D., Martín-Buro, M. C., Hinojosa, J. A., Damme, S. V., et al. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01992-4 · Cited by 19

The impact of weak ground truth and facial expressiveness on affect detection accuracy from time-continuous videos of facial expressions

Tkalčič, M., Odić, A., & Košir, A. (2013) · Information Sciences · doi:10.1016/j.ins.2013.06.006 · Cited by 16

Real vs. Fake Emotion Challenge: Learning to Rank Authenticity from Facial Activity Descriptors

Saxen, F., Werner, P., & Al-Hamadi, A. K. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.363 · Cited by 16

Machine-based Multimodal Pain Assessment Tool for Infants: A ReviewPreprint

Zamzmi, G., Goldgof, D. B., Kasturi, R., Sun, Y., & Ashmeade, T. L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00331 · Cited by 23

Differentiating Between Posed and Spontaneous Expressions with Latent Regression Bayesian Network

Gan, Q., Nie, S., Wang, S., & Ji, Q. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11225 · Cited by 15

Evaluación del dolor mediante el Sistema de Codificación de la Acción Facial. Revisión sistemática

Rojo, R., Prados‐Frutos, J. C., & López‐Valverde, A. (2014) · Medicina Clínica · doi:10.1016/j.medcli.2014.08.010 · Cited by 15

Deep Learned Cumulative Attribute Regression

Jaiswal, S., Egede, J., & Valstar, M. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00113 · Cited by 15

What factors predict anti‐Black bias in pain perception? An internal meta‐analysis across 40 experimental studies

Lin, J., Drain, A., Goharzad, A., & Mende‐Siedlecki, P. (2023) · Social and Personality Psychology Compass · doi:10.1111/spc3.12901 · Cited by 17

Empirically Transformed Energy Patterns: A novel approach for capturing fNIRS signal dynamics in pain assessment

Khan, M. U., Aziz, S., Murtagh, L., Chetty, G., Goecke, R., & Rojas, R. F. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110300 · Cited by 14

Facial grid transformation: A novel face registration approach for improving facial action unit recognition

Han, S., Meng, Z., Liu, P., & Tong, Y. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025283 · Cited by 14

Emotion recognition based on facial components

Rani, P. I., & Muneeswaran, K. (2018) · Sadhana · doi:10.1007/s12046-018-0801-6 · Cited by 14

Capturing global spatial patterns for distinguishing posed and spontaneous expressions

Wang, S., Wu, C., & Ji, Q. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.08.007 · Cited by 13

The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments

Karmakar, A., Khan, M. J., Abdul-Rahman, M. E.-F., & Shahid, U. (2023) · Cureus · doi:10.7759/cureus.44306 · Cited by 13

Recognition of Affective and Grammatical Facial Expressions: A Study for Brazilian Sign Language

da Silva, E. P., Costa, P. D. P., Kumada, K. M. O., Martino, J. M. D., & Florentino, G. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_16 · Cited by 13

Learning to Detect Genuine versus Posed Pain from Facial Expressions using Residual Generative Adversarial Networks

Tavakolian, M., Cruces, C. G. B., & Hadid, A. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756540 · Cited by 12

Cumulative attributes for pain intensity estimation

Egede, J., & Valstar, M. (2017) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3136755.3136789 · Cited by 12

Pain detection from facial expressions using domain adaptation technique

Rathee, N., Pahal, S., & Sheoran, P. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01025-4 · Cited by 11

Assessing the speed and spontaneity of racial bias in pain perception

Mende‐Siedlecki, P., Goharzad, A., Tuerxuntuoheti, A., Reyes, P. G. M., Lin, J., & Drain, A. (2021) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2022.104315 · Cited by 15

A Review of Automatic Lie Detection from Facial Features

Delmas, H., Denault, V., Burgoon, J. K., & Dunbar, N. E. (2024) · Journal of Nonverbal Behavior · doi:10.1007/s10919-024-00451-2 · Cited by 14

Automated Detection of Facial Expressions during Computer-Assisted Instruction in Individuals on the Autism Spectrum

Ahmed, A. A., & Goodwin, M. S. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025472 · Cited by 11

Pain intensity recognition via multi‐scale deep network

Peng, X., Huang, D. J., & Zhang, H. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1448 · Cited by 11

The contemptuous separation: Facial expressions of emotion and breakups in young adulthood

Heshmati, S., Sbarra, D. A., & Mason, A. E. (2017) · Personal Relationships · doi:10.1111/pere.12192 · Cited by 11

Automated Facial Expression based Pain Assessment Using Deep Convolutional Neural Network

Semwal, A., & Londhe, N. D. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9316099 · Cited by 10

A Deep Attention Transformer Network for Pain Estimation with Facial Expression Video

Xu, H., & Liu, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86608-2_13 · Cited by 10

Real and fake emotion detection using enhanced boosted support vector machine algorithm

Annadurai, S., Arock, M., & Vadivel, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13210-6 · Cited by 12

Automated Pain Severity Detection Using Convolutional Neural Network

Semwal, A., & Londhe, N. D. (2018) · International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · doi:10.1109/ctems.2018.8769123 · Cited by 10

Posed and Spontaneous Expression Distinction Using Latent Regression Bayesian Networks

Wang, S., Hao, L., & Ji, Q. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3391290 · Cited by 10

Towards Multimodal Pain Assessment for Research and Clinical Use

Hammal, Z., & Cohn, J. F. (2014) · doi:10.1145/2666253.2666257 · Cited by 10

Credibility, Assessment

Craig, K. D., & Fashler, S. R. (2013) · Springer eBooks · doi:10.1007/978-3-642-28753-4_931 · Cited by 10

Occluded Facial Pain Assessment in the ICU Using Action Units Guided Network

Yuan, X., Cui, Z., Xu, D., Zhang, S., Zhao, C., Wu, X., Jia, T., & Ouyang, B. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3336157 · Cited by 10

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

Capturing Spatial and Temporal Patterns for Distinguishing between Posed and Spontaneous Expressions

Yang, J., & Wang, S. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123350 · Cited by 9

Multi-Scale Regional Attention Networks for Pain Estimation

Cui, S., Huang, D. J., Ni, Y., & Feng, X. (2021) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1145/3473258.3473259 · Cited by 9

Pain estimation with integrating global‐wise and region‐wise convolutional networks

Huang, D. J., Xia, Z., Li, L., & Ma, Y. (2022) · IET Image Processing · doi:10.1049/ipr2.12639 · Cited by 9

Automatic Detection and Intensity Estimation of Spontaneous Smiles

Girard, J. M. (2014) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 9

Deception Detection: The Relationship of Levels of Trust and Perspective Taking in Real-Time Online and Offline Communication Environments

Friend, C., & Hamilton, N. F. (2016) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2015.0643 · Cited by 9

The blenderFace method: video-based measurement of raw movement data during facial expressions of emotion using open-source software

Zinkernagel, A., Alexandrowicz, R., Lischetzke, T., & Schmitt, M. J. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1085-9 · Cited by 9

Microexpression spotting in video using optical strain

Godavarthy, S. (2010) · Digital Commons - University of South Florida (University of South Florida) · Cited by 9

On the Road to the Unconscious Self: Understanding when People Gain Self–knowledge of Implicit Disgust Sensitivity from Behavioural Cues

Zinkernagel, A., Hofmann, W., Gerstenberg, F., & Schmitt, M. J. (2013) · European Journal of Personality · doi:10.1002/per.1910 · Cited by 6

Features Selection for Facial Expression Recognition

Ewees, A. A., ElLaban, H. A., & ElEraky, R. M. (2019) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt45670.2019.8944459 · Cited by 8

Visual and multimodal analysis of human spontaneous behaviour: Introduction to the Special Issue

Pantić, M., & Cohn, J. F. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.07.001 · Cited by 8

Analyses of the Differences between Posed and Spontaneous Facial Expressions

He, M., Wang, S., Liu, Z., & Chen, X. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.20 · Cited by 7

Facial Actions as Social Signals

Valstar, M., Zafeiriou, S. P., & Pantić, M. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316676202.011 · Cited by 7

A Spatio-Temporal Probabilistic Framework for Dividing and Predicting Facial Action Units

Rahman, A. K. M. M., Tanveer, M. I., & Yeasin, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_74 · Cited by 7

Facial Emotion Recognition Based on Eye and Mouth Regions

Rani, P. I., & Muneeswaran, K. (2016) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800141655020x · Cited by 7

Deep Learning for Face Detection and Pain Assessment in Japanese macaques (Macaca fuscata)

Gris, V. N., Crespo, T. R., Kaneko, A., Okamoto, M., Suzuki, J., Teramae, J.-N., & Miyabe‐Nishiwaki, T. (2024) · Journal of the American Association for Laboratory Animal Science · doi:10.30802/aalas-jaalas-23-000056 · Cited by 6

Pain intensity estimation via multimodal fusion: Leveraging ternary textures of derivatives in EDA and PPG signals

Khan, M. U., Hirachan, N., Joseph, C., Murtagh, L., Chetty, G., Goecke, R., & Rojas, R. F. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108532 · Cited by 6

A robust joint face model for human emotion recognition

Hakim, A., Marsland, S., & Guesgen, H. W. (2012) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1145/2425836.2425905 · Cited by 6

Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

Montenegro, J. M. F., Gkelias, A., & Argyriou, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54407-6_17 · Cited by 6

Automated Assessment of Pain: Prospects, Progress, and a Path Forward

Prkachin, K. M., & Hammal, Z. (2021) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3461615.3485671 · Cited by 6

Spontaneous Emotion Detection by Combined Learned and Fixed Descriptors

Racoviţeanu, A., Florea, C., Badea, M., & Vertan, C. (2019) · International Symposium on Signals, Circuits and Systems · doi:10.1109/isscs.2019.8801755 · Cited by 6

Dynamics Are Important for the Recognition of Equine Pain in Video

Broomé, S., Gleerup, K. B., Andersen, P. H., & Kjellström, H. (2019) · 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.2019.01295 · Cited by 8

Automatic spontaneous pain recognition using supervised classification learning algorithms

Rupenga, M., & Vadapalli, H. (2016) · Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) · doi:10.1109/robomech.2016.7813150 · Cited by 5

A novel vision transformer model produces clock drawing test scores as accurate as expert human coders

Hu, M., Qin, T., Gonzalez, R., Freedman, V. A., Zahodne, L. B., Roberto Melipillán, A., & Murphey, Y. L. (2026) · Scientific Reports · doi:10.1038/s41598-025-34064-6 · Cited by 4

Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data

Alphonse, A. S., & Dharma, D. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813314-9.00004-9 · Cited by 4

Creation and validation of the Picture-Set of Young Children’s Affective Facial Expressions (PSYCAFE)

Franz, M., Müller, T., Hahn, S., Lundqvist, D., Rampoldt, D., Westermann, J.-F., Nordmann, M. A., & Schäfer, R. B. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260871 · Cited by 4

Criteria and metrics for thresholded AU detection

Girard, J. M., & Cohn, J. F. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130519 · Cited by 4

Posed and Spontaneous Expression Recognition Through Restricted Boltzmann Machine

Wu, C., & Wang, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-27671-7_11 · Cited by 4

Detection of facial expressions based on Morphological face features and Minimum Distance Classifier

Bozed, K. A., Adjei, O., & Mansour, A. (2013) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta.2013.6783176 · Cited by 4

A Comprehensive Review on - Machine Learning in Bio-Signal Analysis for Chronic Pain Research

Sabeenian, R. S., & Vinodhini, C. M. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434197 · Cited by 4

Efficient Facial Expression Ecognition and classification system based on morphological processing of frontal face images

Apte, A., Basavaraj, A., & Nithin, R. (2015) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2015.7399039 · Cited by 4

Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

Özkan, S., & Akar, G. B. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.366 · Cited by 4

Cognitive Behaviour Analysis Based on Facial Information Using Depth Sensors

Montenegro, J. M. F., Villarini, B., Gkelias, A., & Argyriou, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91863-1_2 · Cited by 3

Convolutional neural networks for posed and spontaneous expression recognition

Xu, C. X., Qin, T., Bar, Y., Wang, G., & Liu, T. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019373 · Cited by 3

Pain Facial Expression Recognition from Video Sequences Using Spatio-temporal Local Binary Patterns and Tracking Fiducial Points

Firouzian, I., Firouzian, N., Hashemi, S., & Kozegar, E. (2020) · International Journal of Engineering · doi:10.5829/ije.2020.33.05b.38 · Cited by 3

Spontaneous facial expression in unscripted social interactions can be measured automaticallyPreprint

Girard, J. M., Cohn, J. F., Jeni, L. A., Sayette, M. A., & la Torre, F. D. (2018) · doi:10.31234/osf.io/gqhjr · Cited by 3

Facial Expressions as a Nexus for Health Assessment

Sooriyaarachchi, J., & Jiang, D. (2026) · Bioengineering · doi:10.3390/bioengineering13020208 · Cited by 2

Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

Chin, S., & Lee, C.-Y. (2013) · International Journal of Advanced Robotic Systems · doi:10.5772/55665 · Cited by 2

Spatiotemporal Facial Super-Pixels for Pain Detection

Lundtoft, D. H., Nasrollahi, K., Moeslund, T. B., & Escalera, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41778-3_4 · Cited by 2

Understanding Human Activities Through 3D Sensors

Montenegro, J. F., Villarini, B., Gkelias, A., & Argyriou, V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91863-1 · Cited by 2

Robust recognition of facial expressions on noise degraded facial images

Sheikh, M. (2011) · University of the Western Cape Electronic Theses and Dissertations Repository (University of the Western Cape) · Cited by 2

From head to toe:body movement for human-computer interaction

Monco, E. F. D. M. (2015) · Lancaster EPrints (Lancaster University) · Cited by 2

Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis

Montenegro, J. M. F. (2018) · Research Repository (Kingston University London) · doi:10.48550/arxiv.1810.10941 · Cited by 3

Denver pain authenticity stimulus set (D-PASS)

Lloyd, E. P., Summers, K. M., Gunderson, C. A., Weesner, R. E., ten Brinke, L., Hugenberg, K., & McConnell, A. R. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02283-2 · Cited by 1

A database of naturalistic expressive faces for studying high arousal states

Buckland, C. B., & Taubert, J. (2024) · Journal of Pain · doi:10.1016/j.jpain.2024.104728 · Cited by 1

Enhancing pain intensity evaluation via an attention-driven channel–spatial fusion network

Gao, F., Qing, L., Li, L., Yang, G., Xu, R., & Gao, L. (2025) · The Visual Computer · doi:10.1007/s00371-025-04167-2 · Cited by 1

Recognition between smiling and neutral facial display with power LBP operator

Nurzyǹska, K., & Bogdan Smolka, B. (2015) · IEEE EUROCON - International Conference on Computer as a Tool (EUROCON) · doi:10.1109/eurocon.2015.7313691 · Cited by 1

IRENE: Context aware mood sharing for social network

Haque, M. M., Adibuzzaman, M., Polyak, D., Jain, N., Ahamed, S. I., & Liu, L. (2011) · IEEE International Conference on Service-Oriented Computing and Applications (SOCA) · doi:10.1109/soca.2011.6166228 · Cited by 1

Shoulder pain intensity recognition using Gaussian mixture models

Majumder, A., Dutta, S., Behera, L., & Subramanian, V. K. (2015) · IEEE International WIE Conference on Electrical and Computer Engineering · doi:10.1109/wiecon-ece.2015.7444016 · Cited by 1

Exaggeration Quantified: An Intensity-Based Analysis of Posed Facial Expressions

Bhaskar, H., Torre, D. L., & Al-Mualla, M. E. (2016) · Springer eBooks · doi:10.1007/978-3-319-25958-1_5 · Cited by 1

An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model

Feng, Z., Chen, M., Zhang, J., & Peng, X. (2024) · Journal of Oral & Facial Pain and Headache · doi:10.22514/jofph.2024.008 · Cited by 1

Mobile based symptom management for palliative care

Ahamed, S. I., & Haque, M. M. (2012) · Cited by 1

Theoretical Approaches Regarding the Psychophysiology of Emotions, Facial Expressions and Emotional Regulation

Ciolacu, M. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.03.348 · Cited by 2

Sistemas Tutores Afetivos: Estado da Arte e Desafios para o Reconhecimento de Emoções Relacionadas à Aprendizagem via Expressões Faciais

Gottardo, E., & Pimentel, A. R. (2016) · doi:10.5753/desafie.2016.9156 · Cited by 1

Automatic person and personality recognition from facial expressions

Martens, L. (2012) · Theses database, Tilburg University · Cited by 1

Commentary: The Dynamic Features of Lip Corners in Genuine and Posed Smiles

Li, Y., Shi, Z., Zhang, H., Luo, L., & Fan, G. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01610 · Cited by 1

Detection of Genuine and Posed Facial Expressions of Emotion: A ReviewPreprint

Jia, S., Wang, S., Hu, C., Webster, P. J., & Li, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.11353 · Cited by 1

Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas

Buzuti, L. F. (2020) · doi:10.31414/ee.2020.d.131151 · Cited by 1

Exploiting Domain-specific Knowledge for Classifier Learning - AU-based Facial Expression Analysis and Emotion Recognition

Seuß, D. (2021) · doi:10.20378/irb-49932 · Cited by 2

Machine Learning Techniques for Automated Analysis of Facial Expressions

Rudovic, O. O. (2013) · doi:10.25560/24677

EMOTIONAL SOPHISTICATION: STUDIES OF FACIAL EXPRESSIONS IN GAMES

Rossi, F. C. D. (2012) · UA Campus Repository (The University of Arizona)

Computational Pain Quantification and the Effects of Age, Gender, Culture and Cause

Ostberg, C. R. (2014) · e-Publications@Marquette (Marquette University)

IT-REHAB : Integral Telerehabilitation System

Fernández-López, H., González-Jiménez, D., González-Castaño, F. J., Rocha, L. A., & Afonso, J. A. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Die Sehnsucht, das Innere des Menschen in seinem Äußeren zu erkennen

Musik, C. (2009) · University of Vienna · doi:10.25365/thesis.6584

Walking the Walk: Exploring the Utility of a Computerized Gait Method in Feigned Symptom Detection

Viklund, A. L. (2015) · doi:10.82465/6578

Sex and gender in pediatric pain: Pain responses and parental modeling

Boerner, K. E. (2016) · DalSpace (Dalhousie University)

Investigating the temporal association between eye actions and smiles

Rupenga, M., & Vadapalli, H. (2016) · Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) · doi:10.1109/robomech.2016.7813132

Image and Vision Computing, 27(12) p. 1741-1844. Automatic Coding of Facial Expressions Displayed During Posed and Genuine Pain

Littlewort, G. C., Bartlett, M. S., & Lee, K. (2013)

Faces of pain

Littlewort, G. C., Bartlett, M. S., & Lee, K. (2007) · International Conference on Multimodal Interfaces · doi:10.1145/1322192.1322198 · Cited by 152

Faces of pain: automated measurement of spontaneous facial expressions of genuine and posed pain

Littlewort, G. C., Bartlett, M. S., & Lee, K. (2007)

Automatic Coding of Facial Expressions of Pain: Are We There Yet?

Lautenbacher, S., Hassan, T., Seuß, D., Loy, F. W., Garbas, J.-U., Schmid, U., & Miriam Kunz, A. (2022) · Pain Research and Management · doi:10.1155/2022/6635496 · Cited by 31

Spontaneous Pain Expression Recognition in Video Sequences

Hammal, Z., Miriam Kunz, A., Arguin, M., & Gosselin, F. (2008) · Electronic workshops in computing · doi:10.14236/ewic/vocs2008.17 · Cited by 20