The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition
Abstract
Temporal information has useful features for recognizing facial expressions. However, to manually design useful features requires a lot of effort. In this paper, to reduce this effort, a deep learning technique, which is regarded as a tool to automatically extract useful features from raw data, is adopted. Our deep network is based on two different models. The first deep network extracts temporal appearance features from image sequences, while the other deep network extracts temporal geometry features from temporal facial landmark points. These two models are combined using a new integration method in order to boost the performance of the facial expression recognition. Through several experiments, we show that the two models cooperate with each other. As a result, we achieve superior performance to other state-of-the-art methods in the CK+ and Oulu-CASIA databases. Furthermore, we show that our new integration method gives more accurate results than traditional methods, such as a weighted summation and a feature concatenation method.
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“In benchmarking experiments across various datasets, many studies found that features extracted with CNNs lead to higher recognition accuracies than handcrafted features (e.g., [87], [88], [89], [90], [91], [64], [92], [93], [94]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [88]
In the citing paper
“This way, researchers artificially increase the number of available examples or training epochs by a factor typically between 10 and 30 [88], [113], [114], and up to 300 [115].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [88]
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Single-Image Facial Expression Recognition Using Deep 3D Re-Centralization
Bao, Z., You, S., Gu, L., & Yang, Z. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00202 · Cited by 6
“According to the subjects of the problem, the tasks can be divided into two categories: image sequence-based and single image-based problems [8, 20].”
BackgroundMethodInfluentialTwo-Channel Feature Extraction Convolutional Neural Network for Facial Expression Recognition
Liu, C., Hirota, K., Wang, B., Dai, Y., & Jia, Z. (2020) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2020.p0792 · Cited by 6
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving ModelsPreprint
Truong, T.-D., Duong, C. N., Luu, K., Tran, M., & Do, M. N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03407 · Cited by 8
Multimodal emotion recognition using deep learning techniques
Nguyen, T. D. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.180753 · Cited by 6
AGILE: Attribute-Guided Identity Independent Learning for Facial Expression Recognition
Aquib, M., Verma, N. K., & Akhtar, M. J. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3508536 · Cited by 5
Quantum Large Language Model Fine-Tuning
Kim, S. H., Mei, J., Girotto, C., Yamada, M., & Roetteler, M. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00258 · Cited by 5
Frontal Facial Expression Recognition using Parallel CNN Model
Deb, S. D., Choudhury, C., Sharma, M., Talukdar, F. A., & Laskar, R. H. (2020) · National Conference on Communications · doi:10.1109/ncc48643.2020.9056011 · Cited by 5
“Some researchers have concentrated on structural information, also known as Geometric Features of the face [19], [20].”
BackgroundFacial Expression Recognition Based on Depth Fusion and Discriminative Association Learning
Jin, X., Lai, Z., Sun, W., & Jin, Z. (2022) · Neural Processing Letters · doi:10.1007/s11063-021-10717-1 · Cited by 5
Yang, P., Yang, H., Wei, Y., & Tang, X. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451764 · Cited by 5
“In Table2, for a comparison purpose, we also tabulate the recognition results of some other state-of-the-art methods using the same CK+ dataset ([17], [18], [19]).”
BackgroundMethodFacial Expression Recognition with Attention Mechanism
Wang, C., Wang, Z., & Cui, D. Y. (2021) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei53629.2021.9624355 · Cited by 5
Gavade, P. A., Bhat, V. S., & Pujari, J. (2021) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip53038.2021.9702545 · Cited by 5
Learning Dynamics for Video Facial Expression Recognition
Wang, Y., Xu, X., & Zhuang, Y. (2021) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3508546.3508581 · Cited by 5
“Among them, [11, 15] are dynamic methods while [3, 19] and our approach are static methods.”
MethodRobust facial expression recognition with global-local joint representation learning
Fan, C., Wang, Z., Li, J., Wang, S., & Sun, X. N. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00907-9 · Cited by 5
“[42] propose a lite deep temporal appearancegeometry network (DTAGN).”
BackgroundImprovement of Negative Emotion Recognition in Visible Images Enhanced by Thermal Imaging
Lee, J.-M., An, Y., Bak, E., & Pan, S. (2022) · Sustainability · doi:10.3390/su142215200 · Cited by 5
Comparing Synchronicity in Body Movement among Jazz Musicians with Their Emotions
Bhave, A., van Delden, J., Gloor, P. A., & Renold, F. K. (2023) · Sensors · doi:10.3390/s23156789 · Cited by 5
“[39] proposed a deep learning approach where one deep network extracted temporal appearance features from image sequences, whereas another deep network focused on”
BackgroundDVC-Net: a new dual-view context-aware network for emotion recognition in the wild
Qing, L., Wen, H., Chen, H., Jin, R., Cheng, Y., & Peng, Y. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09040-8 · Cited by 5
Hybrid Neural Networks Based Approach for Holoscopic Micro-Gesture Recognition in Images and Videos
Sharma, G., Jyoti, S., & Dhall, A. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00130 · Cited by 5
“Here, we applied joint-fine tuning [23].”
Facial Emotion Recognition System for Unusual Behaviour Identification and Alert Generation
Chandran, P. S., & Binu, A. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9509-7_64 · Cited by 5
Fan, J., Wang, S., Yang, P., & Yang, Y. (2020) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45582.2020.9442212 · Cited by 5
Multi-Channel Expression Recognition Network Based on Channel Weighting
Lu, X., Zhang, H., Zhang, Q., & Xue, H. (2023) · Applied Sciences · doi:10.3390/app13031968 · Cited by 5
Wang, C., & Hu, R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534217 · Cited by 5
“In [20], [21], [22], [23], they have performed well by directly using deep features for FER in laboratory-controlled condition.”
MethodUnsupervised Deep Representations for Learning Audience Facial BehaviorsPreprint
Saha, S., Navarathna, R., Helminger, L., & Weber, R. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04136 · Cited by 5
“With recent advancements in deep feature representations [19], supervised deep-learning frameworks are being increasingly used for facial expression recognition and landmark prediction [37, 14, 22, 23, 21, 13, 12, 43, 29, 30].”
Sparse Coding of Shape Trajectories for Facial Expression and Action RecognitionPreprint
Tanfous, A. B., Drira, H., & Amor, B. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03231 · Cited by 5
“For both datasets, we followed the commonly-used experimental setting in [19], [35], [49], [84] consisting on a 10-fold cross validation.”
MethodFacial Expression Recognition Using Human to Animated-Character Expression TranslationPreprint
Ali, K., Isler, I., & Hughes, C. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05595 · Cited by 5
“37 DTAGN[15] Static/7 97.”
MethodAll-In-One: Facial Expression Transfer, Editing and Recognition Using A Single NetworkPreprint
Ali, K., & Hughes, C. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.07050 · Cited by 5
“The reported results show that TER-GAN outperforms state-of-the-art techniques including GAN-based methods like DeRL [39] and deep CNN-based techniques like DTAGN-Joint [11], FN2EN [7], and PPDN [46].”
MethodInfluentialLearnable Graph Inception Network for Emotion Recognition.Preprint
Shirian, A., Tripathi, S., & Guha, T. (2020) · arXiv (Cornell University) · Cited by 5
Automatic Identification of Facial Tics Using Selfie-Video
Loewenstern, Y., Benaroya-Milshtein, N., Belelovsky, K., & Bar‐Gad, I. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3488285 · Cited by 4
Facial Expression Recognition With Label-Noisy Under Dual-Branch Noise Extraction and Suppression
Li, Y., Liu, H., Jiang, D., & Liang, J. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3519359 · Cited by 4
Emotion Recognition in Videos via Fusing Multimodal Features
Chen, S., Dian, Y., Li, X., Lin, X., Jin, Q., Liu, H., & Lü, L. (2016) · Communications in computer and information science · doi:10.1007/978-981-10-3005-5_52 · Cited by 4
Lin, C.-H., Long, F., & Zhan, Y. (2017) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2017.8301920 · Cited by 4
“DTAGN [15] 97.”
Lightweight binary convolutional-transformers fusion network for facial expression recognition
Jin, X., Wu, X., Weng, L., & Ye, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111315 · Cited by 6
Nguyen, M. T., Nguyen, Q. K., Kotani, K., & Siritanawan, P. (2019) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics48868.2019.9023790 · Cited by 4
“Jung et al. [23] in their study deployed two types of convolutional neural networks: one aims for appearance features, one aims for geometry features by taking into consideration facial landmark points.”
BackgroundAudio-Visual Weakly Supervised Approach for Apathy Detection in the Elderly
Sharma, G., Joshi, J., Zeghari, R., & Guerchouche, R. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206829 · Cited by 4
“A number of studies have already proposed different methods to identify emotion based on detected facial expressions from images [24], [25].”
BackgroundGaussian distribution-based facial expression feature extraction network
Wan, F., & Zhi, R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.10.015 · Cited by 4
Realtime Facial Emotion Detection Using Machine Learning
Brintha, N. C., Narayana, J. A., Jaswanth, G., Chandrapal, G. J., & Venkat, D. (2022) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses55317.2022.9914318 · Cited by 4
Saurav, S., Saini, R., & Singh, S. K. (2023) · Computational Intelligence · doi:10.1111/coin.12586 · Cited by 4
“The joint fine-tuning technique employed in the proposed FER framework has initially been proposed for the FER task by Jung et al. 28 and subsequently utilized by the emotion network (EmNet).”
MethodComprehensive review and analysis on facial emotion recognition methods
Vats, R., Kumar, M., Rai, R., & Verma, G. (2023) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.4.040901 · Cited by 4
“Jung et al.31 presented a method that uses deep learning, and also it was considered as a tool for automatic extraction of several features which were useful from raw data.”
Imamura, M., Tashiro, A., Kumano, S., & Otsuka, K. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3491097 · Cited by 4
Multimodal Fusion of Spatial-Temporal Features for Emotion Recognition in the Wild
Wang, Z., & Fang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_20 · Cited by 4
The Role of Coherence in Facial Expression Recognition
Graziani, L., Melacci, S., & Gori, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03840-3_24 · Cited by 4
Facial Expression Recognition Based on Region-Wise Attention and Geometry Difference
Du, H., Zheng, H., & Yu, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03338-5_16 · Cited by 4
Chen, Y., Wang, T., Wu, H., & Wang, Y. (2018) · IEEE International Conference on Intelligence and Safety for Robotics (ISR) · doi:10.1109/iisr.2018.8535644 · Cited by 4
Facial Emotion Detection Using Convolutional Neural Networks
Adil, M. A. (2021) · UVic’s Research and Learning Repository (University of Victoria) · Cited by 4
An Adaptive Landmark-based Attention Network for Students’ Facial Expression Recognition
Wu, Y., & Shen, L. (2021) · International Conference on Communication, Image and Signal Processing (CCISP) · doi:10.1109/ccisp52774.2021.9639303 · Cited by 4
Smart Meet — Facial Recognition-based Conferencing Platform
Krishnan, N., Varghese, A. E., Sankar, V., Achyuth, J. M., Ravikumar, A., & John, J. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760973 · Cited by 4
A Lightweight Network for Expression Recognition Based on Adaptive Mixed Residual Connections
Lu, X., Zhang, H., Zhang, Q., & Xue, H. (2022) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai55851.2022.9904198 · Cited by 4
“Oulu Method Accuracy (%) DTAGN [18] 81.”
InfluentialAttention-Based Global-Local Graph Learning for Dynamic Facial Expression Recognition
Xie, N., Li, J., Guo, M., Yang, L., & Gong, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46305-1_1 · Cited by 4
Acceleration of CNN-Based Facial Emotion Detection Using NVIDIA GPU
Sonawane, B., & Sharma, P. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-7245-1_26 · Cited by 4
Tsai, K.-Y., Ding, J., & Lee, Y.-C. (2018) · IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) · doi:10.1109/apccas.2018.8605689 · Cited by 4
Facial Expression Recognition via Relation-based Conditional Generative Adversarial Network
Song, B. C., Lee, M. K., & Choi, D. Y. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353753 · Cited by 4
“DTAGN [4] 97.25 DeRL [17] 97.30 GCNet [5] 97.”
MethodInfluentialFacial Expression Recognition Based on Multi-dataset Neural Network
Yang, B., Li, Z., & Cao, E. (2020) · Radioengineering · doi:10.13164/re.2020.0259 · Cited by 4
“On the other hand, dynamic-based methods consider the temporal relation among contiguous frames in the input facial expression sequence [5].”
A Comprehensive Study on Facial Expression Recognition Techniques using Convolutional Neural Network
Vasudeva, K., & Chandran, S. (2020) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp48568.2020.9182076 · Cited by 4
“Jung et al.[21] proposed a novel method for Facial Expression Recognition named Deep Temporal Appearance-Geometry Network (DTAGN) which integrates two different models the deep temporal appearance network (DTAN) and the deep temporal geometry network (DTGN).”
MethodA Joint Mapping and Synthesis Approach for Multiview Facial Expression Recognition
Jampour, M., & Moin, M. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421550089 · Cited by 4
An Efficacious Method for Facial Expression Recognition: GAN Erased Facial Feature Network (GE2FN)
Zhang, T., & Tang, K. W. (2021) · International Conference on Machine Learning and Computing (ICMLC) · doi:10.1145/3457682.3457746 · Cited by 4
Clustering-Based Adaptive Dropout for CNN-Based Classification
Wen, Z., Ke, Z., Xie, W., & Shen, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41404-7_4 · Cited by 4
On the pitfalls of learning with limited data: A facial expression recognition case study
Santander, M. R., Albarracín, J. F. H., & Rivera, A. R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114991 · Cited by 4
Multi-velocity neural networks for gesture recognition in videosPreprint
Gupta, O., Raviv, D., & Raskar, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06829 · Cited by 4
“Very recently superior results have been shown [30, 20, 18, 14, 19] using deep neural nets to combine labels and un-labeled data in the same package.”
BackgroundEnhancing Driver Emotion Recognition Through Deep Ensemble Classification
Zaman, F., Xu, Z., Hussain, A., Ullah, A., & Zaman, K. (2025) · Journal of Intelligent and Connected Vehicles · doi:10.26599/jicv.2025.9210055 · Cited by 3
Network Architecture Influence on Facial Emotion Recognition
Arabian, H., Wagner-Hartl, V., & Möeller, K. (2022) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2022-1134 · Cited by 3
Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
Maiden, A., Nguyen, T. V. A., & Nakisa, B. (2025) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta68720.2025.11302420 · Cited by 3
“9680 Deep network with joint fine-tuning [9] CK+ 7 0.”
Deep Learning Based on Face Emotion Recognition using an Artificial Neural Network
Mohan, P., & Chang, H.-T. (2023) · International Conference on Artificial Intelligence and Power Engineering (AIPE) · doi:10.1109/aipe58786.2023.00012 · Cited by 3
“MSR[23] 91.4% 3DCNN DAP [24] 92.4% IB CNN[14] 95.1% IA-CNN [15] 95.3% DTAGN [25] 97.2% Proposed model 97.5%”
InfluentialLopez, D. P., Canal, F. Z., Scotton, G. G., Pozzebon, E., & Sobieranski, A. C. (2024) · Seven Editora eBooks · doi:10.56238/sevened2024.004-006 · Cited by 3
Learning Face Expression Features from Video Using Spatio-Temporal Feature Extractor and CNN-LSTM
Gavade, P. A., Bhat, V. S., & Gavade, A. B. (2023) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537794 · Cited by 3
“According to feature representation FER systems are classified into two main categories: static image FER [2],[3],[4] and Dynamic sequence FER [5],[6],[7].”
BackgroundGarrido, F. B., Herrera, F. A. S., H., P. E. C., & Porras, A. A. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399417 · Cited by 3
“[15] Heechul, J.”
Semi-supervised Emotion Recognition using Inconsistently Annotated Data
Happy, S. L., Dantcheva, A., & Brémond, F. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00075 · Cited by 3
“Over the past years, convolutional neural networks (CNNs) have achieved outstanding performances in many domains of application, including facial expression recognition [4], [5], [6], [7], due to their powerful capacity for modeling complex input patterns.”
BackgroundA benchmark of dynamic versus static methods for facial action unit detection
Alharbawee, L., & Pugeault, N. (2021) · The Journal of Engineering · doi:10.1049/tje2.12001 · Cited by 3
Improved Generative Adversarial Networks for Student Classroom Facial Expression Recognition
Wang, C. (2022) · Scientific Programming · doi:10.1155/2022/9471334 · Cited by 3
“'e literature [14] also proposed model-aware flags for the automatic perception of facial position, and experiments demonstrated that this method not only reduces the workload but also preserves the robustness of the model.”
MethodVisual-based Confusion Detection using a Cooperative Spatio-Temporal Deep Neural Networks
Vanichkul, N., Kobchaisawat, T., & Chalidabhongse, T. H. (2022) · International Conference on Digital Government Technology and Innovation (DGTi-CON) · doi:10.1109/dgti-con53875.2022.9849192 · Cited by 3
“Initially, it was proposed to train two deep neural networks (DNNs) separately and then combine them at the decision level [4].”
BackgroundPTZ-Camera-Based Facial Expression Analysis using Faster R-CNN for Student Engagement Recognition
Komagal, E., & Yogameena, B. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-7169-3_1 · Cited by 3
Deep Spatio-Temporal Decision Fusion Network for Facial Expression Recognition
Chen, X., Yang, H., Zhang, X., Zheng, X., & Li, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-20102-8_9 · Cited by 3
Research on Facial Expression Recognition Algorithm Based on Deep Learning
Zhang, Y., Peng, D., Wang, Y., & Wang, J. (2022) · World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) · doi:10.1109/wcmeim56910.2022.10021491 · Cited by 3
DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features
Wang, T., Zhang, M., & Shang, L. (2023) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg57933.2023.10042668 · Cited by 3
“Other spatiotemporal approaches [7], [13], [24] utilize both spatial and temporal information in expression sequences.”
BackgroundBosch, E., Corbí, R. L. H., Ihme, K. A., Hörmann, S., Jipp, M., & Käthner, D. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3229263 · Cited by 3
Recognition of Masked Facial Expressions Based on Transfer Learning and Data Augmentation
Liu, Y., Zhao, K., & Fu, X. (2023) · International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC) · doi:10.1109/csis-iac60628.2023.10364081 · Cited by 3
“…the temporal geometry features are extracted from temporal facial landmark points by another subnetwork, partial weights of the two subnetworks are frozen, and the unfrozen parts are retrained using a joint fine-tuning method to integrate the two features to recognize facial expressions [42].”
MethodEnhancing Transfer Learning Reliability via Block-Wise Fine-Tuning
Barakat, B., & Huang, Q. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00064 · Cited by 3
“By fine-tuning, these high-performing pre-trained models are now widely used in image generation [11], [14], image classification [15], [16], [17], [18], image caption [12], [13], anomaly detection [19], [20], [21], image retrieval [22], [23], etc.”
MethodSpontaneous thermal facial expression analysis based on trajectory-pooled fisher vector descriptor
Liu, P., & Yin, L. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019315 · Cited by 3
“There are also some deep learning architectures that have been used to jointly perform the feature extraction and recognition [8, 9].”
MethodLearning of Complicate Facial Expression Categories
Zhang, X., & Ma, Y. (2019) · International Conference on Image, Video and Signal Processing (IVSP) · doi:10.1145/3317640.3317659 · Cited by 3
“INTRODUCTION Significant progress has been made in the last decade [1][2][3][4].”
BackgroundDiscriminative Feature Learning Using Two-Stage Training Strategy for Facial Expression Recognition
Gan, L., Zou, Y., & Zhang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30508-6_32 · Cited by 3
“Following the previous work in [1,5], we only use the image sequences captured under normal illumination.”
MethodEmotion Recognition In Videos For Low-Memory Systems Using Deep-Learning
Hagar, A. F., Abbas, H. M., & Khalil, M. I. (2019) · International Conference on Computer Engineering & Systems (ICCES) · doi:10.1109/icces48960.2019.9068168 · Cited by 3
“For example, Jung et al [30] got 92% accuracy with around 178k parameters, while kue et al [31] got 98% accuracy using 2.7 million parameters, both for seven emotions.”
MethodInfluentialUddin, M. A., & Lee, Y.-K. (2020) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp48618.2020.0-104 · Cited by 3
“Similarly, on the CK+ dataset, the proposed fusion approach outperforms the 3D CNN-DAP [30], DNN [8], RBM [9], Expressionlets [10], MSCNN [11] and PHRNN [11] by 5.”
MethodFacial Emotion Recognition Based on Brain and Machine Collaborative Intelligence
Ling, W., Kong, W., Long, Y., Yang, C., & Jin, X. (2019) · IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) · doi:10.1109/civemsa45640.2019.9071606 · Cited by 3
“[19] used two different types of CNN:”
Robust affect analysis using committee of deep convolutional neural networks
Russel, N. S., & Selvaraj, A. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06632-0 · Cited by 3
“The facial expressions are a result of dynamic motions of certain landmark facial points; coordinates of facial landmark points are represented as 1-D trajectory signal [20] or an image-like map [21].”
BackgroundXu, S., Pu, N., Qian, L., & Xiao, G. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7302-1_4 · Cited by 3
Learning head pose-insensitive and discriminative deep features for smile detection
Gan, Y., Chen, J., & Xu, L. (2018) · Journal of Electronic Imaging · doi:10.1117/1.jei.27.5.053048 · Cited by 3
3D convolution network and Siamese-attention mechanism for expression recognition
Zhang, Y., Xia, T., & Liu, Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07860-2 · Cited by 3
“Due to abundant dynamic information contained in image sequences, sequence-based methods, especially deep learning methods [13, 15], achieved better results than previous static image-based methods.”
BackgroundMethodResultInfluentialVisual Scene-Aware Hybrid and Multi-Modal Feature Aggregation for Facial Expression Recognition
Lee, M. K., Kim, D. H., & Song, B. C. (2020) · Sensors · doi:10.3390/s20185184 · Cited by 3
“LMED-CF showed almost the same performance as a SOTA scheme, the deep temporal geometry network (DTGN) [34], in the CK+ dataset, but it significantly outperformed DTGN in the AFEW dataset.”
MethodResultInfluentialEmotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model
Li, D., & Chen, X. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/9991531 · Cited by 3
RETRACTED: Facial expression recognition in classroom environment based on improved Xception model
Tian, J., Fang, J., & Wu, Y. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.051416 · Cited by 3
“EQ-TARGET;temp:intralink-;e014;116;489 v 0 t 1⁄4 vt 1 − β2 ; (14)”
RetractedA Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
Ahmed, T. U., Hossain, S., Hossain, M. S., Islam, R. U., & Andersson, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7597-3_40 · Cited by 3
HSFE Network and Fusion Model based Dynamic Hand Gesture Recognition
Do, N.-T., Na, I. S., & Kim, S. J. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.09.020 · Cited by 3
“Finally, joint fine-tuning fusion [39] described in Fig.”
MethodRealpe-Gómez, J. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.21301.32482 · Cited by 3
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the WildPreprint
Wang, C., Hu, R., Hu, M., Liu, J., Ren, T., He, S., Jiang, M. X., & Miao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11869 · Cited by 3
“[20] [21] [22] [23] have performed well by directly using deep features for FER in laboratorycontrolled condition.”
MethodAn optimized Capsule-LSTM model for facial expression recognition with video sequencesPreprint
Liu, S., Long, Y., Gao, X., Yang, L., Xu, S., Yao, X., & Shu, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07564 · Cited by 3
“3DCNN [22] 60.45 IL-CDD [23] 65.69 CSACNN [24] 71.52 DTGAN [25] 69”
MethodInfluentialDebnath, T., Reza, M. M., Rahman, A., Band, S. S., & Alinejad‐Rokny, H. (2021) · Preprints.org · doi:10.20944/preprints202105.0424.v1 · Cited by 3
Emotion and Sentiment Analysis in Dialogue: A Multimodal Strategy Employing the BERT Model
Parmar, M., & Tiwari, A. (2024) · Parul International Conference on Engineering and Technology (PICET) · doi:10.1109/picet60765.2024.10716061 · Cited by 2
An End-to-End Deep Neural Network for Facial Emotion Classification
Jalal, M. A., Mihaylova, L. S., & Moore, R. K. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011413 · Cited by 2
“al [30] extract temporal image features and temporal geometric features from a series of images using two different CNNs by fine-tuning and fusing them to improve the FER performance.”
MethodMultichannel CNN for Facial Expression Recognition
Trivedi, P., Mhasakar, P. M., Sujata, & Mitra, S. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34869-4_27 · Cited by 2
Song, K.-S., & Kwon, D. (2019) · IEEE RO-MAN · doi:10.1109/ro-man46459.2019.8956436 · Cited by 2
“However, most of the previous studies [13][14]considered only particular types of facial images in which the facial pose was constrained to a frontal or nearfrontal view without considering the speaking effects.”
BackgroundVisual Methods for Sign Language Recognition: A Modality-Based Review
Seddik, B., & Amara, N. E. B. (2021) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-030-81982-8_9 · Cited by 2
Metadata
- Authors
- Jung, H., Lee, S., Yim, J., Park, S., & Kim, J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2983-2991
- OpenAlex
- W2217426128
- Cited by
- 781
- References
- 27
- Citations by year
- 2026: 9 · 2025: 29 · 2024: 45 · 2023: 82 · 2022: 87 · 2021: 127 · 2020: 115 · 2019: 125 · 2018: 92 · 2017: 55 · 2016: 15
- FWCI
- 72.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- KAIST Institutional Repository (KAIST) (submittedVersion)
- 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, joint fine-tuning, facial landmarks, feature integration, image sequences, deep neural network, CK+ database
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