The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs
Abstract
The performance of an automatic facial expression recognition system can be significantly improved by modeling the reliability of different streams of facial expression information utilizing multistream hidden Markov models (HMMs). In this paper, we present an automatic multistream HMM facial expression recognition system and analyze its performance. The proposed system utilizes facial animation parameters (FAPs), supported by the MPEG-4 standard, as features for facial expression classification. Specifically, the FAPs describing the movement of the outer-lip contours and eyebrows are used as observations. Experiments are first performed employing single-stream HMMs under several different scenarios, utilizing outer-lip and eyebrow FAPs individually and jointly. A multistream HMM approach is proposed for introducing facial expression and FAP group dependent stream reliability weights. The stream weights are determined based on the facial expression recognition results obtained when FAP streams are utilized individually. The proposed multistream HMM facial expression system, which utilizes stream reliability weights, achieves relative reduction of the facial expression recognition error of 44% compared to the single-stream HMM system.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Is is rarely used to parametrize FEs for recognition purposes [136], [137].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [137]
In the citing paper
“Most of these early works used geometric representations, like vectors for describing the motion of the face [134], active contours for describing the shape of the mouth and eyebrows [137], or deformable 2D mesh models [116].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [137]
Citations
Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,330
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
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
Automatic facial expression analysis: a survey
Fasel, B., & Luettin, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00052-3 · Cited by 1,810
Example-based learning for view-based human face detection
Sung, K.-K., & Poggio, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655648 · Cited by 1,784
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 1×
“Several automatic facial expression and facial action recognition systems [29]–[ 33 ] utilize FACS as facial features.”
MethodFeature extraction from faces using deformable templates
Yuille, A., Hallinan, P. W., & Cohen, D. S. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127169 · Cited by 1,373
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
Research works printed but not linked: 6
- cohn, 1997, automated face coding: a computer-vision based method of facial expression analysis, 7th Eur Conf Facial Expression Measurement and Meaning, 329
- aleksic, 2005, automatic facial expression recognition using facial animation parameters and multi-stream hmms, 6th E Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 05)
- suwa, 1978, a preliminary note on pattern recognition of human emotional expression, Proc 4th Int Joint Conf on Pattern Recognition, 408
- senior, 1999, face and feature finding for a face recognition system, Proc Int Conf Audio and Video Based Biometric Person Authentication, 154
- aleksic, 2003, an audio-visual person identification and verification system using faps as visual features, Proc Works Multimedia User Authentication, 80
- zhao, 1996, classifying facial emotions by back propagation neural networks with fuzzy inputs, Proc Int Conf Neural Inform Process, 1, 454
Cited by
Sorted by citations
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
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
Facial expression recognition using radial encoding of local Gabor features and classifier synthesis
Gu, W., Xiang, C., Venkatesh, Y. V., Huang, D., & Lin, H. T. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.006 · Cited by 312
Face Expression Recognition and Analysis: The State of the Art
Bettadapura, V. (2012) · arXiv · doi:10.48550/arxiv.1203.6722 · Cited by 336
Facial Expression Recognition Using Facial Movement Features
Zhang, L., & Tjondronegoro, D. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.13 · Cited by 253
Performance Analysis of Multi-Motion Sensor Behavior for Active Smartphone Authentication
Shen, C. Q., Li, Y., Chen, Y., Guan, X., & Maxion, R. A. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737969 · Cited by 203
Bilinear Models for 3-D Face and Facial Expression Recognition
Mpiperis, I., Malassiotis, S., & Strintzis, M. G. (2008) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2008.924598 · Cited by 185
Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition
Vo, T.-H., Lee, G.-S., Yang, H.-J., & Kim, S.-H. (2020) · IEEE Access · doi:10.1109/access.2020.3010018 · Cited by 178
Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning
Shojaeilangari, S., Yau, W., Nandakumar, K., Li, J., & Teoh, E. K. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2416634 · Cited by 151
Uddin, M. Z., Hassan, M. M., Almogren, A., Alamri, A. M., Alrubaian, M., & Fortino, G. (2017) · IEEE Access · doi:10.1109/access.2017.2676238 · Cited by 131
Boosted multi-resolution spatiotemporal descriptors for facial expression recognition
Zhao, G., & Pietikäinen, M. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.03.018 · Cited by 129
Fusion of feature sets and classifiers for facial expression recognition
Zavaschi, T. H. H., Britto, A. S., Oliveira, L. S., & Koerich, A. L. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.074 · Cited by 118
Texture and shape information fusion for facial expression and facial action unit recognition
Kotsia, I., Zafeiriou, S. P., & Pitas, I. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.06.026 · Cited by 106
Uddin, M. Z., Hassan, M. M., Almogren, A., Zuair, M., Fortino, G., & Tørresen, J. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.04.019 · Cited by 103
An enhanced independent component-based human facial expression recognition from video
Uddin, M. Z., Lee, J., & Kim, T.-S. (2009) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2009.5373791 · Cited by 102
Poursaberi, A., Noubari, H. A., Gavrilova, M. L., & Yanushkevich, S. N. (2012) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2012-17 · Cited by 98
Dynamic Facial Expression Recognition Using Longitudinal Facial Expression Atlases
Guo, Y., Zhao, G., & Pietikäinen, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_45 · Cited by 96
Facial expressions classification and false label reduction using LDA and threefold SVM
Shah, J. H., Sharif, M. I., Yasmin, M., & Fernandes, S. L. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.06.021 · Cited by 86
Semantic Audiovisual Data Fusion for Automatic Emotion Recognition
Datcu, D., & Rothkrantz, L. J. M. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch16 · Cited by 90
Facial Expression Recognition Method Based on Improved VGG Convolutional Neural Network
Cheng, S., & Zhou, G. (2019) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420560030 · Cited by 80
A Review on Facial Expression Recognition: Feature Extraction and Classification
Zhao, X., & Zhang, S. (2016) · IETE Technical Review · doi:10.1080/02564602.2015.1117403 · Cited by 78
Hybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition
Chen, H.-Y., Huang, C.-L., & Fu, C.-M. (2007) · IEEE International Conference on Multimedia and Expo · doi:10.1016/j.patcog.2007.08.010 · Cited by 89
Facial-component-based bag of words and PHOG descriptor for facial expression recognition
Li, Z., Imai, J., & Kaneko, M. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346254 · Cited by 67
Salient feature and reliable classifier selection for facial expression classification
Kyperountas, M., Tefas, A., & Pitas, I. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.07.007 · Cited by 66
Intelligent facial emotion recognition using a layered encoding cascade optimization model
Neoh, S. C., Zhang, L., Mistry, K., Hossain, M. A., Lim, C. P., Aslam, N., & Kinghorn, P. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.05.006 · Cited by 59
Agrawal, S., & Khatri, P. (2015) · doi:10.1109/acct.2015.32 · Cited by 58
Prototype-Based Modeling for Facial Expression Analysis
Dahmane, M., & Meunier, J. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2321113 · Cited by 56
Methods for Facial Expression Recognition with Applications in Challenging Situations
Pise, A. A., Alqahtani, M. A., Verma, P., Purushothama, K., Karras, D. A., Prathibha, S., & Halifa, A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9261438 · Cited by 64
Extraction of informative regions of a face for facial expression recognition
Kumar, S., Bhuyan, M. K., & Chakraborty, B. K. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0273 · Cited by 54
An effectual facial expression recognition using HMM
Ramkumar, G., & Logashanmugam, E. (2016) · IEEE International Conference on Advanced Communications, Control and Computing Technologies · doi:10.1109/icaccct.2016.7831590 · Cited by 55
New research advances of facial expression recognition
Liu, S.-S., Tian, Y., & Li, D. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212409 · Cited by 54
Discriminant Graph Structures for Facial Expression Recognition
Zafeiriou, S. P., & Pitas, I. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.2007292 · Cited by 51
Semantic-based facial expression recognition using analytical hierarchy process
Cheng, S., Chen, M.-Y., Chang, H., & Chou, T. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.019 · Cited by 49
Depth Camera-Based Facial Expression Recognition System Using Multilayer Scheme
Siddiqi, M. H., Ali, R., Sattar, A., Khan, A., & Lee, S. (2014) · IETE Technical Review · doi:10.1080/02564602.2014.944588 · Cited by 48
Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods
Jia, S., Wang, S., Hu, C., Webster, P. J., & Li, X. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.580287 · Cited by 59
A Study of Techniques for Facial Detection and Expression Classification
Hemalatha, G., & Sumathi, C. P. (2014) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2014.5203 · Cited by 46
Ouyang, Y., Sang, N., & Huang, R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.03.073 · Cited by 45
Automatic Facial Feature Extraction and Expression Recognition based on Neural Network
P, S., Thool, R. C., & Khandait, P. D. (2011) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2011.020117 · Cited by 44
Siddiqui, M. F. H., Dhakal, P., Yang, X., & Javaid, A. Y. (2022) · Multimodal Technologies and Interaction · doi:10.3390/mti6060047 · Cited by 46
Expression recognition using fuzzy spatio-temporal modeling
Xiang, T., Leung, M. K. H., & Cho, S.-U. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.04.021 · Cited by 41
D-FES: Deep facial expression recognition system
Sharma, S., Kumar, K., & Singh, N. (2017) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/infocomtech.2017.8340635 · Cited by 41
Facial expression recognition with dynamic cascaded classifier
Ashir, A. M., Eleyan, A., & Akdemïr, B. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04138-4 · Cited by 42
Fedorova, K. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/12544.001.0001 · Cited by 40
Facial expression recognition based on anatomy
Eskil, M. T., & Benli, K. S. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.11.002 · Cited by 43
Kalita, J., & Das, K. (2013) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2013.040229 · Cited by 39
E2‐capsule neural networks for facial expression recognition using AU‐aware attention
Cao, S., Yao, Y., & An, G. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0063 · Cited by 39
Jagadeesh, M., & Baranidharan, B. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00957-z · Cited by 40
Hough Forest-Based Facial Expression Recognition from Video Sequences
Fanelli, G., Yao, A., Noel, P.-L., Gall, J., & Gool, L. V. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35749-7_15 · Cited by 39
Lin, D.-T., & Pan, D.-C. (2009) · Integrated Computer-Aided Engineering · doi:10.3233/ica-2009-0304 · Cited by 39
Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems
Siddiqi, M., Lee, S., Lee, Y.-K., Khan, A., & Truc, P. T. H. (2013) · Sensors · doi:10.3390/s131216682 · Cited by 38
Novel Multiclass Classifiers Based on the Minimization of the Within-Class Variance
Kotsia, I., Pitas, I., Zafeiriou, S., & Zafeiriou, S. (2008) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2004376 · Cited by 36
A Local-Global Graph Approach for Facial Expression Recognition
Kakumanu, P., & Bourbakis, N. (2006) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2006.15 · Cited by 31
Facial Expression Recognition Based on SVM in E-learning
Chen, L., Zhou, C., & Shen, L. (2012) · IERI Procedia · doi:10.1016/j.ieri.2012.06.171 · Cited by 32
Analysis of Facial Emotion Recognition
Balasubramanian, B., Diwan, P., Nadar, R., & Bhatia, A. (2019) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei.2019.8862731 · Cited by 33
WiFace: Facial Expression Recognition Using Wi-Fi Signals
Chen, Y., Ou, R., Li, Z., & Wu, K. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3001989 · Cited by 31
Study of Facial Micro-expressions in Psychology
Bhushan, B. (2014) · Springer eBooks · doi:10.1007/978-81-322-1934-7_13 · Cited by 36
Interface Framework to Drive an Intelligent Wheelchair Using Facial Expressions
Faria, P. M., Braga, R. A. M., Valgôde, E., & Reis, L. P. (2007) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2007.4374877 · Cited by 29
Facial expression recognition based on image pyramid and single-branch decision tree
Ashir, A. M., & Eleyan, A. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-016-1052-9 · Cited by 27
Automatic facial feature extraction and expression recognition based on neural networkPreprint
Khandait, S. P., Thool, R. C., & Khandait, P. D. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1204.2073 · Cited by 25
Facial Expression Recognition Using Facial-component-based Bag of Words and PHOG Descriptors
Li, Z., Imai, J., & Kaneko, M. (2010) · Journal of information processing · doi:10.11185/imt.5.1003 · Cited by 24
Experiments with Facial Expression Recognition using Spatiotemporal Local Binary Patterns
Zhao, G., & Pietikäinen, M. (2007) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2007.4284844 · Cited by 24
Personalized facial expression recognition in indoor environments
Chang, C., & Huang, Y. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596316 · Cited by 24
Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition
Jia, Q., Gao, X., Guo, H., Luo, Z., & Wang, Y. (2015) · Sensors · doi:10.3390/s150306719 · Cited by 23
Liu, Z., Li, S., Cao, W., Li, D., Hao, M., & Ri, Z. (2019) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2019.p0444 · Cited by 23
Analysis on techniques used to recognize and identifying the Human emotions
Kulkarni, P., & Rajesh, T. M. (2020) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v10i3.pp3307-3314 · Cited by 20
Huang, X., Zhao, G., Pietikäinen, M., & Zheng, W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17691-3_29 · Cited by 18
Advances in facial expression recognition technologies for emotion analysis
Jayaswal, R., Ansari, M. A., Dixit, M., Singh, D. K., & Ahmad, S. (2025) · Discover Computing · doi:10.1007/s10791-025-09699-8 · Cited by 25
Facial Expression Recognition Using Facial-component-based Bag of Words and PHOG Descriptors
Li, Z., Imai, J., & Kaneko, M. (2010) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.64.230 · Cited by 18
Uddin, M. Z., & Hassan, M. M. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1793-1 · Cited by 18
Facial 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
Image Segmentation Based on Minimal Spanning Tree and Cycles
Janakiraman, T. N., & Mouli, C. (2007) · International Conference on Computational Intelligence and Multimedia Applications (ICCIMA) · doi:10.1109/iccima.2007.54 · Cited by 18
Montazeri, S., & Seyyedsalehi, S. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.05.016 · Cited by 18
The Applications of Facial Expression Recognition in Human-computer Interaction
Wang, H., & Gu, J. (2018) · IEEE International Conference on Advanced Manufacturing (ICAM) · doi:10.1109/amcon.2018.8614755 · Cited by 18
A comprehensive study on Facial Expressions Recognition Techniques
Jameel, R., Singhal, A., & Bansal, A. (2016) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence.2016.7508167 · Cited by 17
Video-Based Facial Expression Recognition using Deep Temporal–Spatial Networks
Pan, X., Zhang, S., Guo, W., Zhao, X., Chuang, Y., Chen, Y., & Zhang, H. (2019) · IETE Technical Review · doi:10.1080/02564602.2019.1645620 · Cited by 17
Affective Computing and Biometrics Based HCI Surveillance System
Wang, R., & Fang, B. (2008) · International Symposium on Information Science and Engineering · doi:10.1109/isise.2008.317 · Cited by 17
Coupled Dictionary Learning for the Detail-Enhanced Synthesis of 3-D Facial Expressions
Liang, H., Liang, R., Song, M., & He, X. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2417211 · Cited by 16
Learning spatial weighting for facial expression analysis via constrained quadratic programming
Liao, C., Chuang, H.-J., Duan, C.-H., & Lai, S. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.03.017 · Cited by 16
Roshanzamir, M., Jafari, M., Alizadehsani, R., Roshanzamir, M., Shoeibi, A., Górriz, J. M., Khosravi, A., Nahavandi, S., & Acharya, U. R. (2024) · Information Systems Frontiers · doi:10.1007/s10796-023-10466-7 · Cited by 17
Automatic Generation of Facial Expression Using Triangular Geometric Deformation
Sheu, J., Hsieh, T. S., & Shou, H.-N. (2014) · Journal of Applied Research and Technology · doi:10.1016/s1665-6423(14)71671-2 · Cited by 15
Physiological Sensing for Affective Computing
Peter, C., Ebert, E., & Beikirch, H. (2008) · Springer eBooks · doi:10.1007/978-1-84800-306-4_16 · Cited by 14
Facial Expression Recognition Based on Selective Feature Extraction
Zhou, G., Zhan, Y., & Zhang, J. (2006) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2006.253872 · Cited by 15
Martínez‐Rodrigo, A., Zangróniz, R., Pastor, J. M., & Sokolova, M. V. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.04.006 · Cited by 14
Analytical review of methods for emotion recognition by human face expressions
Рюмина, Е. В., & Karpov, A. A. (2020) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2020-20-2-163-176 · Cited by 14
Fast gesture recognition with Multiple Stream Discrete HMMs on 3D skeletons
Borghi, G., Vezzani, R., & Cucchiara, R. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899766 · Cited by 13
Uddin, M. Z. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2614-5 · Cited by 12
Facial Emotion Detection Using Convolutional Neural Networks
Hosur, V., & Desai, A. (2022) · IEEE Mysore Sub Section International Conference (MysuruCon) · doi:10.1109/mysurucon55714.2022.9972510 · Cited by 12
Facial Expression Recognition Based on Hybrid Features and Fusing Discrete HMMs
Zhan, Y., & Zhou, G. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73335-5_45 · Cited by 12
Dual-graph regularized discriminative transfer sparse coding for facial expression recognition
Chen, D., & Song, P. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102906 · Cited by 12
The decadal perspective of facial emotion processing and Recognition: A survey
Rathour, N., Singh, R., Gehlot, A., Akram, S. V., Thakur, A. K., & Kumar, A. (2022) · Displays · doi:10.1016/j.displa.2022.102330 · Cited by 14
Research on the selection of kernel function in SVM based facial expression recognition
Wang, F., He, K., Liu, Y., Li, L., & Hu, X. (2013) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2013.6566586 · Cited by 11
Li, Z., Imai, J., & Kaneko, M. (2010) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2010.5664923 · Cited by 11
Facial Expression Recognition System
Ren, Y. (2008) · UWSpace (University of Waterloo) · Cited by 11
Combining appearance and geometric features for facial expression recognition
Yu, H. L., & Liu, H. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2179066 · Cited by 10
Hidden Markov Model Decision Forest for Dynamic Facial Expression Recognition
Liang, J., Xu, C., Feng, Z., & Ma, X. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001415560108 · Cited by 10
Zernike Moment-Based Facial Expression Recognition Using Two-Staged Hidden Markov Model
Rahul, M., Shukla, R., Yadav, D. K., & Yadav, V. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-6861-5_56 · Cited by 10
Person-independent facial expression recognition via hierarchical classification
Xue, M., Liu, W., & Li, L. (2013) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2013.6529832 · Cited by 9
Facial Signs and Psycho-physical Status Estimation for Well-being Assessment
Chiarugi, F., Iatraki, G., Christinaki, E., Manousos, D., Giannakakis, G. A., Pediaditis, M., Pampouchidou, A., Kostas Marias, A., & Tsiknakis, M. (2014) · International Conference on Health Informatics · doi:10.5220/0004934405550562 · Cited by 10
Hybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition
Chen, H.-Y., Huang, C., & Fu, C.-M. (2007) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2007.4284739 · Cited by 9
Aleksic, P. S., Potamianos, G., & Katsaggelos, A. K. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374456-2.00024-4 · Cited by 9
Self-citation
Metadata
- Authors
- Aleksic, P. S., & Katsaggelos, A. K.
- Affiliations
- Northwestern University (US)
- ORCID
- Katsaggelos, A.K.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2006-03
- Volume
- 1
- Issue
- 1
- Pages
- 3-11
- ISSN
- 1556-6013 (ISSN-L)
- OpenAlex
- W2098044144
- Cited by
- 235
- References
- 34
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 11 · 2021: 1 · 2020: 14 · 2019: 8 · 2018: 7 · 2017: 15 · 2016: 11 · 2015: 20 · 2014: 19 · 2013: 19 · 2012: 12
- FWCI
- 24.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › 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
- facial expression recognition, facial animation parameters, eyebrow movements, MPEG-4, error reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
