A novel database of children's spontaneous facial expressions (LIRIS-CSE)
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The dataset in [80] does not contain images from different ethnicities, which hinders model generalization.
“for instance the data collected in [80] does not contain the images from different ethnicities which will hinder models generalization.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [80]
Re-identification pulls down the illusion of privacy policies promised by technologists.
“Re-identification pulls down the illusion of privacy policies, which are promised by technologists.”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [154]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works
Ekman, P. (1976) · Medical Entomology and Zoology · Cited by 4,345 · see citing works
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303 · see citing works
Places: A 10 Million Image Database for Scene Recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042 · see citing works
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2015) · doi:10.1109/cvpr31182.2015 · Cited by 3,735 · see citing works
Ekman, P. (1993) · American Psychologist · doi:10.1037/0003-066x.48.4.384 · Cited by 3,441 · see citing works
Universals and cultural differences in facial expressions of emotion.
Ekman, P. (1972) · Medical Entomology and Zoology · Cited by 2,273 · see citing works
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213 · see citing works
Nebraska Symposium on Motivation, 1969.
Schwartz, S. H., Arnold, W. J., & Levine, D. I. (1971) · American Sociological Review · doi:10.2307/2093748 · Cited by 2,185 · see citing works
2011 International Joint Conference on Neural Networks
Boubacar, H. A., Lecoeuche, S., & Maouche, S. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2063350 · Cited by 1,481 · see citing works
The History Began from AlexNet: A Comprehensive Survey on Deep Learning ApproachesPreprint
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01164 · Cited by 1,190 · see citing works
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 1,099 · see citing works
A Brief Review of Facial Emotion Recognition Based on Visual Information
Ko, B. C. (2018) · Sensors · doi:10.3390/s18020401 · Cited by 747 · see citing works
A Spontaneous Micro-expression Database: Inducement, collection and baseline
Li, X., Pfister, T., Huang, X., Zhao, G., & Pietikäinen, M. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553717 · Cited by 703 · see citing works
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657 · cited 1× · see citing works
“Recently, most of successful models are based on deep learning approach [29, 30, 31], specifically on Convolutional Neural Network (CNN) architecture [15].”
Computer Vision and Pattern Recognition 2020
Akata, Z., Geiger, A., & Sattler, T. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01522-3 · Cited by 646 · see citing works
Keltner, D. J., Ekman, P., Gonzaga, G. C., & Beer, J. S. (2002) · doi:10.1093/oso/9780195126013.003.0022 · Cited by 555 · see citing works
Bassili, J. N. (1979) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.37.11.2049 · Cited by 531 · see citing works
Bassili, J. N. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2049 · Cited by 466 · see citing works
Visualizing and Understanding Convolutional Neural NetworksPreprint
Zeiler, M. D., & Fergus, R. (2013) · arXiv (Cornell University) · Cited by 440 · see citing works
The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults
LoBue, V., & Thrasher, C. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01532 · Cited by 341 · see citing works
Human Computing and Machine Understanding of Human Behavior: A Survey
Pantić, M., Pentland, A., Nijholt, A., & Huang, T. S. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_3 · Cited by 318 · see citing works
EGGER, H. L., Pine, D. S., Nelson, E. E., Leibenluft, E., Monique Ernst, D., Towbin, K. E., & Angold, A. C. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.343 · Cited by 295 · see citing works
An optimal frequency domain filter for edge detection in digital pictures
(1979) · NCHU Institutional Repository CRIS · doi:10.1109/tpami · Cited by 198 · see citing works
Chen, L., Zhou, M., Su, W., Wu, M., She, J., & Hirota, K. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.044 · Cited by 196 · see citing works
Tracking through Life Stages: Adult, Immature and Juvenile Autumn Migration in a Long-Lived Seabird
Péron, C., & Grémillet, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072713 · Cited by 158 · see citing works
Human computing and machine understanding of human behavior
Pantić, M., Pentland, A., Nijholt, A., & Huang, T. S. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181044 · Cited by 153 · see citing works
Framework for reliable, real-time facial expression recognition for low resolution images
Khan, R. A., Meyer, A., Konik, H., & Bouakaz, S. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.03.022 · Cited by 144 · see citing works
Self-citationFace expression recognition with a 2-channel Convolutional Neural Network
Hamester, D., Barros, P. V. A., & Wermter, S. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280539 · Cited by 140 · see citing works
The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set
Dalrymple, K. A., Gomez, J., & Duchaine, B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079131 · Cited by 137 · see citing works
Children's recognition of disgust in others.
Widen, S. C., & Russell, J. A. (2013) · Psychological Bulletin · doi:10.1037/a0031640 · Cited by 134 · see citing works
What Do We Understand About Convolutional Networks?Preprint
Hadji, I., & Wildes, R. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08834 · Cited by 127 · see citing works
Machine analysis of facial behaviour: naturalistic and dynamic behaviour
Pantić, M. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0135 · Cited by 103 · see citing works
Deep Meta-Learning: Learning to Learn in the Concept SpacePreprint
Zhou, F., Wu, B., & Li, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03596 · Cited by 95 · see citing works
2006 IEEE International Conference on Multimedia and Expo
(2006) · doi:10.1109/icme10990.2006 · Cited by 86 · see citing works
Emotional Expressions of Young Infants and Children
Sullivan, M. W., & Lewis, M. (2003) · Infants & Young Children · doi:10.1097/00001163-200304000-00005 · Cited by 78 · see citing works
A Prototype for Automatic Recognition of Spontaneous Facial Actions
Bartlett, Littlewort, G. C., B, T. J. S. A., & Movellan, J. R. (2002) · neural information processing systems · Cited by 73 · see citing works
Real-time Convolutional Neural Networks for Emotion and Gender ClassificationPreprint
Arriaga, O., Valdenegro-Toro, M., & Plöger, P. G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07557 · Cited by 59 · see citing works
Measuring Liquid-Level Utilizing Wedge Wave
MATSUYA, I., Honma, Y., Mori, M., et al. (2017) · Sensors · doi:10.3390/s18010002 · Cited by 54 · see citing works
Saliency-based framework for facial expression recognition
Khan, R. A., Meyer, A., Konik, H., & Bouakaz, S. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-017-6114-9 · Cited by 43 · see citing works
Self-citationBebis, G. N., Boyle, R., Parvin, B., Koracin, D. R., Pavlidis, I. T., Feris, R., McGraw, T., Elendt, M., Kopper, R., Ragan, E. D., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27863-6 · Cited by 28 · see citing works
Khan, R. A., Meyer, A., Konik, H., et al. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239186 · Cited by 15 · see citing works
Self-citationDifferentiating 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 · see citing works
Automatic Affect Analysis: From Children to Adults
Khan, R. A., Meyer, A., & Bouakaz, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27863-6_28 · Cited by 9 · see citing works
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Research works printed but not linked: 4
- [5][5] M. Valstar, M. Pantic, Induced disgust, happiness and surprise: an addition to the MMI facial expression database, in: International Language Resources and Evaluation Conference, 2010. -
- [38][38] K. Simonyan, A. Zisserman, Very deep convolutional networks for largescale image recognition, in: Arxiv, 2014. -
- [40][40] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, Y. LeCun, Overfeat: Integrated recognition, localization, and detection using convolutional networks, in: International Conference on Learning Representations, 2014. -
- [43][43] S. J. Pan, Q. Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering 22 (10) (2010) 1345-1359. -
Cited by
Sorted by citations
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Self-citationEthical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
Self-citationA Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD)
Sharif, H., & Khan, R. A. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2004655 · Cited by 10 · see citing works
Self-citation
- Authors
- Khan, R. A., Crenn, A., Meyer, A., & Bouakaz, S.
- Affiliations
- Lyon 1 Université (FR)
- Corresponding author
- Khan, Rizwan Ahmed
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 83-84
- Pages
- 61-69
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2903181347
- Cited by
- 82
- References
- 54
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 11 · 2023: 11 · 2022: 15 · 2021: 14 · 2020: 7 · 2019: 6
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Image and Vision Computing (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Région Auvergne-Rhône-Alpes
- 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 › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- spontaneous facial expressions, children, facial expression recognition, video databases, basic emotions, convolutional neural networks, transfer learning
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