Active appearance models

Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2603342 · Cited by 6,214 · see citing works

The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression

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

Incremental Learning for Robust Visual Tracking

Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061 · see citing works

A review of affective computing: From unimodal analysis to multimodal fusion

Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644 · see citing works

Synthesizing Obama: learning lip sync from audio

Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073640 · Cited by 1,057 · see citing works

Evaluation of prostate segmentation algorithms for mri: The promise12 challenge

Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857 · see citing works

BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database

Zhang, X., Yin, L., Cohn, J. F., Canavan, S., Reale, M., Horowitz, A., Liu, P., & Girard, J. M. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.06.002 · Cited by 745 · see citing works

The Code for Facial Identity in the Primate Brain

Chang, L., & Tsao, D. Y. (2017) · Cell · doi:10.1016/j.cell.2017.05.011 · Cited by 644 · see citing works

Expressing fear enhances sensory acquisition.

Susskind, J. M., Lee, D. H., Cusi, A., Feiman, R., Grabski, W., & Anderson, A. K. (2008) · Nature Neuroscience · doi:10.1038/nn.2138 · Cited by 595 · see citing works

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

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

Detecting depression from facial actions and vocal prosody

Cohn, J. F., Kruez, T. S., Matthews, I., Yang, Y., Nguyen, M. H., Padilla, M. T., Zhou, F., & De la Torre, F. (2009) · 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops · doi:10.1109/acii.2009.5349358 · Cited by 494 · see citing works

Multi-PIE

Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2008) · 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition · doi:10.1109/afgr.2008.4813399 · Cited by 373 · see citing works

Realtime performance-based facial animation

Weise, T., Bouaziz, S., Li, H., & Pauly, M. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964972 · Cited by 356 · see citing works

The painful face – Pain expression recognition using active appearance models

Ashraf, A. B., Lucey, S., Cohn, J. F., Chen, T., Ambadar, Z., Prkachin, K. M., & Solomon, P. E. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.05.007 · Cited by 355 · see citing works

Face swapping: automatically replacing faces in photographs

Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., & Nayar, S. K. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360638 · Cited by 333 · see citing works

A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations

Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.07.001 · Cited by 330 · see citing works

Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification

Rodriguez, P., Cucurull, G., Gonzalez, J., Gonfaus, J. M., Nasrollahi, K., Moeslund, T. B., & Roca, F. X. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2662199 · Cited by 297 · see citing works

Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Jain, A., Koppula, H. S., Raghavan, B., Soh, S., & Saxena, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.364 · Cited by 274 · see citing works

Medical image segmentation on GPUs – A comprehensive review

Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C., & Lindseth, F. (2015) · Medical Image Analysis · doi:10.1016/j.media.2014.10.012 · Cited by 264 · see citing works

Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses

Girard, J. M., Cohn, J. F., Mahoor, M. H., Mavadati, S. M., Hammal, Z., & Rosenwald, D. P. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2013.12.007 · Cited by 255 · see citing works

Continuous Pain Intensity Estimation from Facial Expressions

Kaltwang, S., Rudovic, O., & Pantic, M. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33191-6_36 · Cited by 245 · see citing works

AffectAura: an intelligent system for emotional memory

McDuff, D., Karlson, A., Kapoor, A., Roseway, A., & Czerwinski, M. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208525 · Cited by 242 · see citing works

Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree

Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205 · see citing works

Facial Expression Recognition: A Survey

Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195 · see citing works

Automatic detection of pain intensity

Hammal, Z., & Cohn, J. F. (2012) · Proceedings of the 14th ACM international conference on Multimodal interaction · doi:10.1145/2388676.2388688 · Cited by 185 · see citing works

Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face

Lucey, S., Bilal, A., & F., J. (2007) · Face Recognition · doi:10.5772/4841 · Cited by 161 · see citing works

LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood

Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00826 · Cited by 165 · see citing works

RSGAN: face swapping and editing using face and hair representation in latent spaces

Natsume, R., Yatagawa, T., & Morishima, S. (2018) · ACM SIGGRAPH 2018 Posters · doi:10.1145/3230744.3230818 · Cited by 140 · see citing works

A survey on face data augmentation for the training of deep neural networks

Wang, X., Wang, K., & Lian, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04748-3 · Cited by 141 · see citing works

A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods

Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142 · see citing works

Pose estimation for spacecraft relative navigation using model-based algorithms

Opromolla, R., Fasano, G., Rufino, G., & Grassi, M. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2650785 · Cited by 127 · see citing works

Facial expression recognition using Gabor motion energy filters

Wu, T., Bartlett, M. S., & Movellan, J. R. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543267 · Cited by 126 · see citing works

Error weighted semi-coupled hidden Markov model for audio-visual emotion recognition

Lin, J.-C., Wu, C.-H., & Wei, W.-L. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2171334 · Cited by 121 · see citing works

Medical Image Segmentation: Methods and Software

Withey, D. J., & Koles, Z. J. (2007) · 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging · doi:10.1109/nfsi-icfbi.2007.4387709 · Cited by 113 · see citing works

Training an Active Random Field for Real-Time Image Denoising

Barbu, A. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2028254 · Cited by 105 · see citing works

Relative pose estimation for cylinder-shaped spacecrafts using single image

Liu, C., & Hu, W. (2014) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2014.120757 · Cited by 101 · see citing works

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

Ingle, P. Y., & Kim, Y.-G. (2022) · Sensors · doi:10.3390/s22103862 · Cited by 96 · see citing works

Computing Accurate Correspondences across Groups of Images

Cootes, T. F., Twining, C. J., Petrovic, V. S., Babalola, K. O., & Taylor, C. J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.193 · Cited by 70 · see citing works

Incorporating prior knowledge in medical image segmentation: a survey

Nosrati, M. S., & Hamarneh, G. (2016) · arXiv · doi:10.48550/arxiv.1607.01092 · Cited by 55 · see citing works

Expression Recognition Method Based on a Lightweight Convolutional Neural Network

Zhao, G., Yang, H., & Yu, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964752 · Cited by 53 · see citing works

Estimating smile intensity: A better way

Girard, J. M., Cohn, J. F., & De la Torre, F. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.004 · Cited by 44 · see citing works

Design and evaluation of a foveated video streaming service for commodity client devices

Ryoo, J., Yun, K., Samaras, D., Das, S. R., & Zelinsky, G. (2016) · Proceedings of the 7th International Conference on Multimedia Systems · doi:10.1145/2910017.2910592 · Cited by 41 · see citing works

A multimodal fuzzy inference system using a continuous facial expression representation for emotion detection

Soladié, C., Salam, H., Pelachaud, C., Stoiber, N., & Séguier, R. (2012) · Proceedings of the 14th ACM international conference on Multimodal interaction · doi:10.1145/2388676.2388782 · Cited by 33 · see citing works

A unified probabilistic framework for automatic 3D facial expression analysis based on a Bayesian belief inference and statistical feature models

Zhao, X., Dellandréa, E., Zou, J., & Chen, L. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.10.001 · Cited by 27 · see citing works

Hands-Free User Interface for AR/VR Devices Exploiting Wearer’s Facial Gestures Using Unsupervised Deep Learning

Cha, J., Kim, J., & Kim, S. (2019) · Sensors · doi:10.3390/s19204441 · Cited by 12 · see citing works

The development of the Athens Emotional States Inventory (AESI): collection, validation and automatic processing of emotionally loaded sentences

Chaspari, T., Soldatos, C., & Maragos, P. (2015) · The World Journal of Biological Psychiatry · doi:10.3109/15622975.2015.1012228 · Cited by 8 · see citing works

Who do you want to be? Real-time face swap

den Uyl, T. M., Tasli, H. E., Ivan, P., & Snijdewind, M. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163172 · Cited by 5 · see citing works