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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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Items 401–500 of 779
Towards an “In-the-Wild” Emotion Dataset Using a Game-Based Framework
Li, W., Abtahi, F., Tsangouri, C., & Zhu, Z. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.190 · Cited by 11
“An example is Jung, et al’s work [8] in which facial landmarks based shape information and image based appearance information are learned through a combined CNN network.”
BackgroundFacial Expression Recognition with Skip-Connection to Leverage Low-Level Features
Verma, M., Kobori, H., Nakashima, Y., Takemura, N., & Nagahara, H. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803396 · Cited by 11
“We present detailed comparison of the proposed method with DTAGN [3], CNN-DNN Fig.”
BackgroundMethodInfluentialAn Efficient Algorithm of Facial Expression Recognition by TSG-RNN Network
Huang, K., Li, J., Cheng, S., Yu, J., Tian, W., Zhao, L., Hu, J., & Chang, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37734-2_14 · Cited by 11
Multi-View Facial Expression Recognition with Multi-View Facial Expression Light Weight Network
Jie, S., & Yongsheng, Q. (2020) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661820040197 · Cited by 11
“The former ones usually rely on accurate detection of eyes, eyebrows, nose, and lips [1–3].”
BackgroundMethodInfluentialSubspace learning for facial expression recognition: an overview and a new perspective
Turan, C., Zhao, R., Lam, K., & He, X. (2021) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2020.27 · Cited by 11
Human Abnormality Classification Using Combined CNN-RNN Approach
Kabir, M. M., Safir, F. B., Shahen, S., Maua, J., Awlad, I. A. B., & Mridha, M. F. (2020) · IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET) · doi:10.1109/honet50430.2020.9322814 · Cited by 11
“The analysis of facial expressions is a laborious task for Deep Learning (DL) approaches, because the human being can deflect the way they express their expressions significantly [3, 4].”
MethodMulti-spectral Facial Landmark Detection
Keong, J., Dong, X., Jin, Z., Mallat, K., & Dugelay, J. (2020) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs49906.2020.9360890 · Cited by 11
“One of the main prerequisites of facial image analysis is facial landmark detection as it plays a key role in many tasks, such as automatic face recognition [18] [8], expression recognition [9], 3D face reconstruction [3].”
BackgroundFacial Expression Recognition Using Machine Learning Techniques
Ullah, S., Jan, A., & Khan, G. M. (2021) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet53442.2021.9659631 · Cited by 16
Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
Soman, G., Vivek, M., Judy, M. V., Papageorgiou, E. I., & Gerogiannis, V. C. (2022) · Algorithms · doi:10.3390/a15020055 · Cited by 11
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
Frame attention networks for facial expression recognition in videosPreprint
Meng, D., Peng, X., Wang, K., & Qiao, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.00193 · Cited by 11
“ST network [23] S: the last frame T: all frames S: the last frame T: all frames 98.50 DTAGN [12] Fixed length Fixed length 97.25 CNN+Island loss[24] The last three frames and the first frame The last three frames and the first frame”
BackgroundMethodInfluentialDebnath, T., Reza, M. M., Rahman, A., Band, S. S., & Alinejad‐Rokny, H. (2021) · Research Square · doi:10.21203/rs.3.rs-511221/v1 · Cited by 11
Lightweight Facial Expression Recognition Based on Class-Rebalancing Fusion Cumulative Learning
Mou, X., Song, Y., Wang, R., Tang, Y., & Xin, Y. (2023) · Applied Sciences · doi:10.3390/app13159029 · Cited by 10
LSTPNet: Long short-term perception network for dynamic facial expression recognition in the wild
Lu, C., Jiang, Y., Fu, K., Zhao, Q., & Yang, H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.104915 · Cited by 10
Landmark-Based Adaptive Graph Convolutional Network for Facial Expression Recognition
Zhao, D., Wang, J., Li, H., & Wang, D. (2024) · IEEE Access · doi:10.1109/access.2024.3463176 · Cited by 10
“In this section, we compare the proposed LBAGCN with state-of-the-art methods [10], [12], [29], [30], [31], [32], [33] on CK + and Oulu CASIA in terms of parameters, computational cost (FLOPs) and accuracy.”
MethodResultInfluentialFacial Expression Recognition With Heatmap Neighbor Contrastive Learning
Liu, T., Li, J., Wu, J. X., Du, B., Zhan, Y., Tao, D., & Wan, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543029 · Cited by 10
Facial Expression Recognition Based on Spatial-Temporal Fusion with Attention Mechanism
Zhang, L., Zheng, X., Chen, X., Ren, X., & Ji, C. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11129-5 · Cited by 10
“Compared with DTAGN [39] (utilizing LBP) and WMDNN [40] (optical-flow based features), our model only uses the appearance features of facial expression sequences without any manual feature extraction, but our recognition accuracy is still better than them.”
MethodTransformer embedded spectral-based graph network for facial expression recognition
Jin, X., Song, X., Wu, X., & Yan, W. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02016-z · Cited by 10
“It confirm that using spatial-temporal feature from video sequences achieved better performance, e.g., DTAN [67] and CDLM-frame2seq [63]).”
Deep learning enhanced attributes conditional random forest for robust facial expression recognition
Liao, H., Wang, D., Fan, P., & Ding, L. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10951-8 · Cited by 10
“The two popular approaches for features extraction are: geometric based [20, 30, 35].”
BackgroundFacial Motion Analysis beyond Emotional Expressions
Porta-Lorenzo, M., Vázquez-Enríquez, M., Pérez-Pérez, A., Alba‐Castro, J. L., & Docío-Fernández, L. (2022) · Sensors · doi:10.3390/s22103839 · Cited by 10
“Existing deep learning approaches for video-based FER from facial landmarks, typically concatenate their coordinates over multiple frames to form a sequence of vectors as the input to Recurrent Neural Networks (RNNs) [18], or rearrange them to form an image-like matrix to feed a Convolutional Neural Networks (CNNs) [1…”
BackgroundA recursive framework for expression recognition: from web images to deep models to game dataset
Li, W., Tsangouri, C., Abtahi, F., & Zhu, Z. (2018) · Machine Vision and Applications · doi:10.1007/s00138-017-0904-9 · Cited by 11
“An example is Jung, et al’s work [18] in which facial land-”
BackgroundShen, W., & Li, X. (2020) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-07-2020-0088 · Cited by 10
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression RecognitionPreprint
Kacem, A., Daoudi, M., Amor, B. B., & Alvarez-Paiva, J. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06440 · Cited by 10
“Here again, the protocol used in [14, 20] was followed according to a 10-fold crossvalidation schema.”
BackgroundMethodInfluentialFacial Key points Detection using MobileNetV2 Architecture
Kulkarni, U., Gurlahosur, S. V., Babar, P. P., Muttagi, S. I., Soumya, N. S., Jadekar, P., & Meena, S. M. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126381 · Cited by 10
“The traditional methods to extract features such as Local Binary Pattern (LBP) [5], Gabor filters [6], and Histogram of Oriented Gradients (HOG) [7] are commonly used and provide good results.”
Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
Li, Y., Liu, H., Liang, J., & Jiang, D. (2025) · Applied Sciences · doi:10.3390/app15095139 · Cited by 9
Ravi Raj, K. G., & Demirkol, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-22835-0 · Cited by 14
“Jung et al. 37 present a collaborative fine-tuning in DNNs for FER.”
Learning Spatio-Temporal Features With Partial Expression Sequences for On-the-Fly Prediction
Baddar, W. J., & Ro, Y. M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12332 · Cited by 9
“As shown in the table, the proposed method outperformed existing stateof-the-art FER methods.”
BackgroundMethodInfluentialGan, C., Yao, J., Ma, S., Zhang, Z., & Zhu, L. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.07.009 · Cited by 9
Facial Expression Recognition Based on Multi-Channel Attention Residual燦etwork
Shen, T., & Xu, H. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.022312 · Cited by 10
Common Latent Embedding Space for Cross-Domain Facial Expression Recognition
Wang, R., Song, P., Li, S., Ji, L., & Zheng, W. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3276990 · Cited by 9
“Jung et al. [11] employ convolution neural network (CNN) for FER.”
BackgroundProjective complex matrix factorization for facial expression recognition
Duong, V.-H., Lee, Y.-S., Ding, J., Pham, B.-T., Bui, M.-Q., Bao, P. T., & Wang, J. (2018) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-017-0521-9 · Cited by 9
Comparing Methods for Assessment of Facial Dynamics in Patients with Major Neurocognitive Disorders
Wang, Y., Dantcheva, A., Broutart, J.-C., Robert, P. H., Brémond, F., & Biliński, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_10 · Cited by 9
“[14] use two neural networks separately to extract temporal appearance features, as well as temporal geometric features for expression recognition.”
BackgroundMethodMicro‐expression recognition by two‐stream difference network
Pan, H., Xie, L., Li, J., Lv, Z., & Wang, Z. (2021) · IET Computer Vision · doi:10.1049/cvi2.12030 · Cited by 9
A cascaded spatiotemporal attention network for dynamic facial expression recognition
Ye, Y., Pan, Y., Liang, Y., & Pan, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03781-0 · Cited by 9
ARM: a lightweight module to amend facial expression representation
Shi, J., Zhu, S., Wang, D., & Liang, Z. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02339-4 · Cited by 11
“[12] designed an integration network to process facial images and landmark points jointly.”
BackgroundRashmi Adyapady, R., & Annappa, B. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13940-7 · Cited by 9
“Oulu CASIA DTAGN (Joint) [18] Sequence-based 81.”
Werner, P., Saxen, F., Al-Hamadi, A. K., & Yu, H. L. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756596 · Cited by 9
“The simplest form is cropping the face bounding box obtained by face detection and rescaling it to a canonical size [19], [5] (we later refer to this as FaceDet).”
BackgroundA Hybrid Facial Expression Recognition System Based on Recurrent Neural Network
Guo, J.-M., Huang, P., & Chang, L.-Y. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909888 · Cited by 9
Facial Expression Recognition by Jointly Partial Image and Deep Metric Learning
Yu, N., & Bai, D. (2019) · IEEE Access · doi:10.1109/access.2019.2963201 · Cited by 9
“In particular, most of the fear emotions were confused with surprise, which is the same as the results of other works [2], [16], [20], but our model has a higher recognition rate for other expressions.”
BackgroundResultDeep Learning Based Approach of Emotion Detection and Grading System
Sonawane, B., & Sharma, P. (2020) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661820040239 · Cited by 9
“Few studies [28], [29] observed that integration of the results from multiple CNNs leads to better result.”
BackgroundDeep video gesture recognition using illumination invariantsPreprint
Gupta, O., Raviv, D., & Raskar, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06531 · Cited by 9
“Over the past decade algorithms for training neural nets have dramatically evolved, allowing us to efficiently train deep neural nets [Hinton et al. 2006; Jung et al. 2015].”
BackgroundDeep Multi-Facial Patches Aggregation Network For Facial Expression RecognitionPreprint
Hazourli, A. R., Djeghri, A., Salam, H., & Othmani, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09298 · Cited by 9
“(2015) [24] Deep Temporal Appearance-Geometry Network (DTAGN) CK+ 97.”
ResultJoint Deep Cross-Domain Transfer Learning for Emotion RecognitionPreprint
Nguyen, D., Sridharan, S., Nguyen, D. T., Denman, S., Tran, S. N., Zeng, R., & Fookes, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11136 · Cited by 9
“Subjects were asked to express 6 discrete emotions: anger, disgust, fear, happiness, sadness, and surprise Martin et al. (2006). The SAVEE dataset Haq and Jackson (2009) is an audio-visual dataset which was recorded by higher degree researchers (aged from 27 to 31 years) at the University of Surrey, and four native ma…”
BackgroundUsing Self-Supervised Co-Training to Improve Facial Representation.Preprint
Pourmirzaei, M., Esmaili, F., & Montazer, G. A. (2021) · arXiv (Cornell University) · Cited by 9
Spatial deep feature augmentation technique for FER using genetic algorithm
Nida, N., Yousaf, M. H., Irtaza, A., Javed, S., & Velastín, S. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09245-x · Cited by 8
Liu, K.-H., Shih, X.-K., Liu, T.-J., & Liu, W.-R. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394018 · Cited by 8
“The performance of FER methods [23, 24] has been improving on laboratory-controlled datasets, but there is still a lot of room for improvement on in-the-wild datasets.”
MethodCross-centroid ripple pattern for facial expression recognition
Verma, M., & Vipparthi, S. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19364-9 · Cited by 9
“Other CNN-based systems such as DTAGN [32], DTAGN-Joint [33], Inception [34], Spatiotemporal [35], GCNet [36], encoder-decoder FER [37], MaNet [38], and AutoFERNet [39] have also achieved accelerated growth in the field of facial expression recognition.”
A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect
Hu, S., Bhattacharya, H., Chattopadhyay, M., Aslam, N., & Shum, H. P. H. (2018) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima.2018.8631537 · Cited by 8
“Works in the facial expression recognition literature [13] [14] uses only convolution neural network.”
Ullah, A., Wang, J., Anwar, M. S., Ahmad, U., Saeed, U., & Fei, Z. (2019) · Electronics · doi:10.3390/electronics8121487 · Cited by 8
“methods: LBP-Top [38], HOG 3D [39], 3D CNN [40], STM-Explet [41], DTAGN [42], and CNN [43].”
Özdemir, R., & Koç, M. (2019) · Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi · doi:10.35193/bseufbd.645138 · Cited by 8
Robust Emotion Navigation: Few-shot Visual Sentiment Analysis by Auxiliary Noisy Data
Wang, L., Xu, X., Liu, F., Xing, X., Cai, B., & Lu, W. (2019) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2019.8925021 · Cited by 8
“The visual sentiment analysis mainly focus on human/animal facial expression recognition [1]–[3].”
BackgroundContext-Aware Emotion Recognition in the Wild Using Spatio-Temporal and Temporal-Pyramid Models
Do, N.-T., Kim, S.-H., Yang, H.-J., Lee, G.-S., & Yeom, S. (2021) · Sensors · doi:10.3390/s21072344 · Cited by 10
“We propose the best selection ensemble and compare it to average fusion and join fine-tuning fusion [10].”
MethodInfluentialJob offers recommender system based on virtual organizations
González‐Briones, A., Chamoso, P., Pavón, J., la Prieta, F. D., & Corchado, J. M. (2022) · Expert Systems · doi:10.1111/exsy.13152 · Cited by 8
“In both cases, softmax is the selected activation function (Jung et al., 2015), which is defined in Equation (4). y i represents the result of the ANN for neuron i of the output layer.”
BackgroundA Deep Learning Approach for Human Facial Expression Recognition using Residual Network – 101
Kumari, R., & Wasim, J. (2023) · Journal of Current Science and Technology · doi:10.59796/jcst.v13n3.2023.2152 · Cited by 8
Sultana, M., Bhatti, N., Javed, S., & Jung, S. K. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.5.053017 · Cited by 8
Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition
Wang, Y., Li, M., Wan, X., Zhang, C., & Wang, Y. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8886872 · Cited by 8
“In addition, to achieve a high recognition accuracy, feature learning [58] and different joint representations of features [59] are also used in their own network model.”
MethodPan, Y., Cai, K., Cheng, M. S., Zou, X., & Li, M. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412766 · Cited by 8
Image Perturbation-Based Deep Learning for Face Recognition Utilizing Discrete Cosine Transform
Park, J., & Kim, K. (2021) · Electronics · doi:10.3390/electronics11010025 · Cited by 9
Friji, R., Chaieb, F., Drira, H., & Kurtek, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3291663 · Cited by 7
Li, J., Yang, L., Lv, C., Chu, Y., & Liu, Y. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3540321 · Cited by 7
Face Detection from Blurred Images based on Convolutional Neural Networks
Roozbahani, K. M., & Hamid Soltanian-zadeh, A. (2022) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip53647.2022.9738783 · Cited by 7
Transfer Learning-Based Independent Component Analysis
Zheng, Z., Zhao, W., Hable, B., Gong, Y., Wang, X., Shannon, R. W., & Liu, K. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3229294 · Cited by 7
Faces are Domains: Domain Incremental Learning for Expression Recognition
Maharjan, R. S., Romeo, M., & Cangelosi, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191542 · Cited by 8
“the face into one of the six basic emotions [22], [23].”
BackgroundSingh, N. T., Ritu, R., Kaur, C., & Chaudhary, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307556 · Cited by 7
“A system using two ConvNet models with distinct properties was suggested by H. Jung et al. [27].”
MethodImproving recommendation diversity without retraining from scratch
Noel, J., Monterola, C. P., & Tan, D. S. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00518-9 · Cited by 7
Chen, W., & Hu, H. (2018) · Electronics Letters · doi:10.1049/el.2018.7235 · Cited by 7
Facial Expression Recognition With Confidence Guided Refined Horizontal Pyramid Network
Su, W., Zhang, H., Su, Y., & Yu, J. (2021) · IEEE Access · doi:10.1109/access.2021.3069468 · Cited by 7
Ni, R., Liu, X., Chen, Y., Zhou, X., Cai, H., & Kiong, L. C. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.026813 · Cited by 7
Kim, N., Cho, S., & Bae, B. (2022) · Sensors · doi:10.3390/s22155753 · Cited by 9
Facial Emotion Recognition Using HOG and Convolution Neural Network
Ghalib, R., Ibrahim, A.-W. S., & Noor, A. A. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280118 · Cited by 7
“The spatiotemporal characteristic might be based on geometric characteristics as well as appearance [4, 5].”
Attention-Guided Network Model for Image-Based Emotion Recognition
Arabian, H., Battistel, A., Chase, J. G., & Möeller, K. (2023) · Applied Sciences · doi:10.3390/app131810179 · Cited by 8
Identity-Enhanced Network for Facial Expression Recognition
Li, Y., Wang, X., Zhang, S., Xie, L., Wu, W., Yu, H., & Zhu, Z. D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20870-7_33 · Cited by 7
“Among all the compared databases, our proposed IDEnNet outperforms the state-of-the-art methods including handcraft-based methods (LBP-TOP [28], and HOG 3D [9]), video-based methods (MSR [19], AdaLBP [27], Atlases [5], STM-ExpLet [13], and DTAGN [7]), and CNN-based methods (3D-CNN [12], 3D-CNN-DAP [12], DTAGN [7], PPD…”
MethodSalih, W. M., Ibraheem, I. N., & Tariq, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-38752-5_19 · Cited by 7
Unconstrained Facial Expression Recognition Based on Feature Enhanced CNN and Cross-Layer LSTM
Tong, Y., Chen, R., & Liang, R. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2020edl8065 · Cited by 7
“Jung et al. [10] proposed a joint fine-tuning network method based on two di ff erent models to improve the recognition accuracy.”
MethodG2-VER: Geometry Guided Model Ensemble for Video-based Facial Expression Recognition
Albrici, T., Fasounaki, M., Salimi, S. B., Vray, G., Bozorgtabar, B., Ekenel, H. K., & Thiran, J. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756600 · Cited by 7
“[12] designed a Deep Temporal Geometry Network (DTGN), which has shown that face landmark trajectory fed”
BackgroundMicro-Expression Recognition Using Micro-Variation Boosted Heat Areas
Zhang, M., Huan, Z., & Shang, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60639-8_44 · Cited by 7
Farokhah, L. (2020) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v4i6.2644 · Cited by 7
“Metode non peak expression atau permasalahan intensitas emosi mulai dikembangkan untuk mengikuti kejadian di kehidupan nyata STMExplet[15], LOMo[16], IACNN[17], BDBN[18], Facenet2expnet[19], DTAGN[20], PPDN[21].”
BackgroundFace-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/RecognitionPreprint
Papadopoulos, K., Kacem, A., Shabayek, A. E. R., & Aouada, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09145 · Cited by 7
“These landmarks describe well the face deformations and have been extensively used for face expression analysis [12, 14].”
BackgroundUsing Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial RepresentationPreprint
Pourmirzaei, M., Montazer, G. A., & Esmaili, F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4395579 · Cited by 8
“Another study [24], used two different types of CNN networks on videos that improve FER performance.”
Baddar, W. J., & Ro, Y. M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013215 · Cited by 6
“The experiment was conducted under 10-fold subject-independent cross validation and the prediction (Kim et al. 2017; Jung et al. 2015).”
MethodTemporal-Contrastive Appearance Network for Facial Expression Recognition
Li, Z., Liu, Y.-H., Liu, A.-S., Yang, Y.-H., Yeh, T.-H., & Fu, L. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00405 · Cited by 6
“Compared with DTAGN whose accuracy of the fearful is only 84%, but TCAN has reached 100%.”
BackgroundMethodInfluentialOptimized, robust, real-time emotion prediction for human-robot interactions using deep learning
Jaiswal, S., & Nandi, G. C. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12794-3 · Cited by 6
E2E-MFERC: A Multi-Face Expression Recognition Model for Group Emotion Assessment
Wang, L., Zhao, J., Hu, S., & Xu, X. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048688 · Cited by 6
Madhan, S., Sridharan, S., Deivasigamani, S., Rajesh, R., & Surendran, R. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722685 · Cited by 6
“Everyone is looking for methods to quantify stress over extended periods of time without interfering with workers' daily routines[4].”
BackgroundFace Reenactment Based Facial Expression Recognition
Ali, K., & Hughes, C. E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64556-4_39 · Cited by 6
Ullah, A., Wang, J., Anwar, M. S., Whangbo, T. K., & Zhu, Y. (2021) · Journal of Sensors · doi:10.1155/2021/8893661 · Cited by 6
A Frown-Based Thermal Comfort Detection Method of Facial Emotion Recognition
Zhang, Q., Tang, K. W., Liu, X., Wang, Q., & Ouchi, K. (2021) · International Conference on Machine Learning and Machine Intelligence (MLMI) · doi:10.1145/3490725.3490726 · Cited by 6
“[6] proposed a method that can learn both the temporal appearance features and temporal geometry features by training two independent networks for the first time.”
MethodFused deep learning based Facial Expression Recognition of students in online learning mode
Sumalakshmi, C., & Vasuki, P. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7137 · Cited by 7
“FI = ∂ (WVFVt + WHFHt−1 + WMIMt−1 + BF) (26)”
Dual Expression Fusion: A Universal Microexpression Recognition Framework
Sun, X. N., Lang, J., Zheng, S., Huang, Y., & Wang, M. (2022) · IEEE Multimedia · doi:10.1109/mmul.2022.3187695 · Cited by 6
Facial Expression Recognition Based on A Lightweight CNN Model
Chen, Q., Jing, X., Zhang, F., & Mu, J. (2022) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb55706.2022.9828739 · Cited by 6
Growth Pattern Fingerprinting for Automatic Analysis of Lung Adenocarcinoma Overall Survival
Alsubaie, N. M., Raza, S. E. A., Snead, D. R. J., & Rajpoot, N. (2023) · IEEE Access · doi:10.1109/access.2023.3251220 · Cited by 6
“Otherwise fine-tuning might be an alternative solution [32],”
BackgroundSSER: Semi-Supervised Emotion Recognition based on Triplet Loss and pseudo label
Pan, L., Shao, W., Xiong, S., Lei, Q., Huang, S., Beckman, E., & Hu, Q. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111595 · Cited by 7
Two-Level Attention with Multi-task Learning for Facial Emotion Estimation
Wang, X., Peng, M., Pan, L., Hu, M. Q., Jin, C., & Ren, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05710-7_19 · Cited by 6
A Survey of Facial Expression Recognition Based on Convolutional Neural Network
Song, L. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940228 · Cited by 6
“Facial expression recognition consists of three parts: (a) Face detection and localization [4][5]; (b) Facial features extraction [6][7]; (c) Classification of facial expressions [8].”
BackgroundSocial Network Recommender System, A Neural Network Approach
Rivas, A., Chamoso, P., González‐Briones, A., Pavón, J., & Corchado, J. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62365-4_21 · Cited by 6
Zhou, J., Zhang, X., Lin, Y., & Liu, Y. M. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2020.103018 · Cited by 6
Dynamic versus Static Facial Expressions in the Presence of Speech
Salman, A. N., & Busso, C. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00119 · Cited by 6
“These results provide important insights for FER, challenging practices to process frame-by-frame the facial images in a video, and supporting practices that attempt to extract temporal information [12], [13] or compensate for lexical information [14]–[16].”
ResultSurface mounted devices classification using a mixture network of DCNN and DFCN
Liu, W., Sun, H., Jia, Z., & Yu, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.011 · Cited by 6
Convolutional Gate Recurrent Unit for Video Facial Expression Recognition in the Wild
Kang, K., & Ma, X. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866235 · Cited by 7
“Compared with traditional approaches designing pre-defined features, CNN has demonstrated considerable advancement in robustness [7] and accuracy [28] in processing images.”
MethodA Versatile Online System for Person-specific Facial Expression Recognition
Nayak, S., Happy, S. L., Routray, A., & Sarma, M. (2019) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2019.8929422 · Cited by 6
“[22] deep temporal geometry network 92.”
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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