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Video-based emotion recognition in the wild using deep transfer learning and score fusion
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Items 101–200 of 234
Multimodal Deep Models for Predicting Affective Responses Evoked by Movies
Thao, H. T. P., Herremans, D., & Roig, G. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00201 · Cited by 20
“In most current research in video-based emotion recognition [29, 23, 30, 31, 5], multimodal approaches have been applied to integrate information from different modalities.”
BackgroundEmotion recognition using support vector machine and one-dimensional convolutional neural network
Sujanaa, J., Palanivel, S., & Balasubramanian, M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11041-5 · Cited by 18
“In recent years, CNN architecture [11, 14, 15] and pre-trained models like VGG16, VGG19, etc. are widely used for their promising performances.”
BackgroundA Deep Learning Facial Emotion Classification system: a VGGNet-19 based approach
Abbassi, N., Helaly, R., Hajjaji, M. A., & Mtibaa, A. (2020) · International Conference on Sciences and Techniques of Automatic Control and Computer Engineering · doi:10.1109/sta50679.2020.9329355 · 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
A high-efficiency energy and storage approach for IoT applications of facial recognition
Peixoto, S. A., Vasconcelos, F. F. X., Guimarães, M. T., Medeiros, A. G., Rêgo, P. A. L., Neto, A. V. L., de Albuquerque, V. H. C., & Filho, P. P. R. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103899 · Cited by 17
AttendAffectNet–Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention
Thao, H. T. P., Balamurali, B. T., Roig, G., & Herremans, D. (2021) · Sensors · doi:10.3390/s21248356 · Cited by 17
“It is also a common approach in recent studies on emotion recognition of humans in videos [66,122,123] and on evoked emotion prediction from movies [15,19,21], in which features extracted from video and audio are used.”
BackgroundAn Efficient Long Chinese Text Sentiment Analysis Method Using BERT-Based Models with BiGRU
Sheng, D., & Yuan, J. (2021) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd49262.2021.9437789 · Cited by 17
“There are several types of fusion techniques, including feature-level fusion, decision-level fusion, hy brid fusion, model-level fusion rule-based fusion, and classification-based fusion [9], [10].”
BackgroundMultimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions
Kaya, H., & Salah, A. A. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_10 · Cited by 16
Self-citationVideo-based emotion recognition in the wild
Salah, A. A., Kaya, H., & Gürpınar, F. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-814601-9.00031-6 · Cited by 15
Self-citationA bottom-up summarization algorithm for videos in the wild
Pan, G., Zheng, Y., Zhang, R., Han, Z., Sun, D., & Qu, X. (2019) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-019-0611-y · Cited by 15
Derin Öğrenme Araştırma Alanlarının Literatür Taraması
Yapıcı, M. M., Tekerek, A., & Topaloğlu, N. (2019) · Gazi Journal of Engineering Sciences · doi:10.30855/gmbd.2019.03.01 · Cited by 14
Barros, P. V. A., Barakova, E. I., & Wermter, S. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3002657 · Cited by 14
“Existing solutions address the problem of continual learning in deep learning models by introducing transfer learning techniques [21], [31], neural activation and data distribution regularization [32], [33], and the”
BackgroundPandeya, Y. R., Bhattarai, B., & Lee, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-98856-2 · Cited by 17
Emotion Recognition Based on Streaming Real-Time Video with Deep Learning Approach
Martha, S., Mohmmad, S., Kothandaraman, D., Ramesh, D., & Kanakam, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1976-3_48 · Cited by 13
Towards Engagement Recognition of People with Dementia in Care Settings
Steinert, L., Putze, F., Küster, D., & Schultz, T. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418856 · Cited by 13
“This finding is in line with other studies which reported the benefits of using CNN features [6, 13, 24] and especially when they are based on the fine-tuned VGGFace network [13, 20, 21].”
BackgroundMethodResultAttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies
Thao, H. T. P., Balamurali, B. T., Herremans, D., & Roig, G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412835 · Cited by 13
“Studies in emotion recognition of humans in videos have applied a multimodal approach using audio, visual and text features [34], [35], [36].”
MethodAn Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wildPreprint
Dresvyanskiy, D., Рюмина, Е. В., Kaya, H., Markitantov, M., Karpov, A. A., & Minker, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03692 · Cited by 13
“This output of the finetuned model is used inline with the former works [9, 16, 10] After extracting frame-level features from each aligned face using the deep neural network, non-overlapping short chunks of the original videos are summarized by computing mean and standard deviation statistics of each dimension over t…”
BackgroundMethodInfluentialSelf-citationGupta, S., Kumar, P., & Tekchandani, R. (2023) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2023.11.001 · Cited by 13
Joint Discrete and Continuous Emotion Prediction Using Ensemble and End-to-End Approaches
AlBadawy, E. A., & Kim, Y. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242972 · Cited by 12
“Particularly, the dataset has been used in the AVEC [34, 44] and has resulted in numerous advancements in emotion recognition [2, 17, 18, 20, 23].”
MethodManuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels
Khayyat, M. M., & Elrefaei, L. A. (2020) · IEEE Access · doi:10.1109/access.2020.3010882 · Cited by 13
“[67] fused the audio and visual features extracted from video inputs using a weighted score-level fusion model.”
MethodContinuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
Teixeira, T., Granger, É., & Koerich, A. L. (2021) · Applied Sciences · doi:10.3390/app112411738 · Cited by 12
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System
Ruiz, N., Yu, H., Allessio, D., Jalal, M., Joshi, A., Murray, T., Magee, J., Whitehill, J., Ablavsky, V., Arroyo, I., et al. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9667001 · Cited by 13
Multimodal emotional analysis through hierarchical video summarization and face tracking
Thiruthuvanathan, M. M., & Krishnan, B. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11010-y · Cited by 12
Emotional Health Detection in HAR: New Approach Using Ensemble SNN
Bibbò, L., Cotroneo, F., & Vellasco, M. M. B. R. (2023) · Applied Sciences · doi:10.3390/app13053259 · Cited by 11
Video Emotion Recognition using Hand-Crafted and Deep Learning Features
Xia, X., Liu, J., Yang, T., Jiang, D., Han, W., & Hichem Sahli, B. (2018) · Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia) · doi:10.1109/aciiasia.2018.8470326 · Cited by 11
“Normally these local face descriptors are aggregated into a global video descriptor by using certain pooling mechanisms, e.g., Fisher Vector [20], Bag of Words [21], Vector of Locally Aggregated Descriptors [22], etc. Deep learning based features usually utilize Deep Convolutional Neural Networks (DCNNs) to learn abst…”
BackgroundTarasov, A. V., & Savchenko, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-11027-7_19 · Cited by 11
Bimodal Emotion Recognition Based on Audio and Facial Parts Using Deep Convolutional Neural Networks
Cornejo, J. Y. R., & Pedrini, H. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00026 · Cited by 10
“[21] introduced an approach to multimodal emotion recognition in the wild.”
BackgroundDetermine Bipolar Disorder Level from Patient Interviews Using Bi-LSTM and Feature Fusion
Ebrahim, M., Al‐Ayyoub, M., & Alsmirat, M. A. (2018) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams.2018.8554886 · Cited by 12
“[3] modeled these features using Partial Least Squares (PLS) regression and Extreme Learning Machines classifiers as they are both fast and accurate [16], [17].”
MethodA large-scale television advertising dataset for detailed impression analysis
Li, T., Nakamura, S., Wang, X., Tatsuya, K., Tamura, G., & Yamasaki, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14704-7 · Cited by 9
Cháves-Villota, A., Jiménez-Martín, A., Jojoa, M., Bahillo, A., & García-Domínguez, J. J. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101873 · Cited by 13
Neural network model for video-based facial expression recognition in-the-wild on mobile devices
Demochkina, P., & Savchenko, A. V. (2021) · International Conference on Information Technology and Nanotechnology (ITNT) · doi:10.1109/itnt52450.2021.9649076 · Cited by 9
Krithika, L. B., & Priya, G. G. L. (2018) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.09.011 · Cited by 9
“Here, the Convolutional Neural Network (CNN) was mainly utilized to detect the facial features from the input for video recognition.”
InfluentialEmotion Recognition using AutoEncoders and Convolutional Neural Networks
Prieto, L. A. B., & Oplatková, Z. K. (2018) · MENDEL · doi:10.13164/mendel.2018.1.113 · Cited by 9
“Most of the research in emotion detection focuses on the usage of a deep learning technique alone [26, 27, 28, 29].”
BackgroundHappiness Intensity Estimation for a Group of People in Images using Convolutional Neural Networks
Lu, G., & Zhang, W. (2019) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1109/eitce47263.2019.9094832 · Cited by 9
Real-time video based emotion recognition using convolutional neural network and transfer learning
Sujanaa, J. (2020) · Indian Journal of Science and Technology · doi:10.17485/ijst/v13i31.1118 · Cited by 9
“Extended Cohn-Kanade Dataset (CK+), Facial Expression Recognition 2013 (FER-2013), Denver Intensity of Spontaneous Facial Action Database (DIFSA), Japanese Female Facial Expression Dataset (JAFFE), MMI Facial Expression Database, Emotion Recognition in the Wild Challenge (EmotiW) dataset, Radboud Faces Database (RaFD)…”
MethodAnanthanagu, U., & Agarwal, P. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7660-5_4 · Cited by 9
Deep 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
Review of the Open Data Sets for Contactless Sensing
Liang, K., Chen, J., He, T., Wang, W. W., Singh, A. K., Rawat, D. B., Song, H., & Lv, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3351838 · Cited by 10
“For example, Kwon et al. [61] used video captured by a smartphone to monitor heart rate, while a video-based algorithm for emotion recognition was developed by Kaya et al. [62].”
MethodAdvances in plasma spraying technology for coatings: From particle flight to splat formation
Liu, K., Guo, M., Ye, G., Wang, Y., Fan, W., Du, J., Cao, Y., & Zhang, L. (2025) · Applied Materials Today · doi:10.1016/j.apmt.2025.103061 · Cited by 8
Can mood primitives predict apparent personality?
Soğancıoğlu, G., Kaya, H., & Salah, A. A. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597444 · Cited by 8
“A deep neural network pre-trained with VGG-Face [35] and fine-tuned with FER-2013 database [36] is used from [37].”
MethodSelf-citationAffective Computing of Childern with Authism Based on Feature Transfer
Han, J., Li, X., Xie, L., Liu, J., Wang, F., & Wang, Z. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691180 · Cited by 8
Context-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
“[59] CNN-FUN Features + Kernel ELMPLS Spatiotemporal (3D) Visual 2017 51.”
BackgroundAn Automatic Multimedia Likability Prediction System Based on Facial Expression of Observer
Bawa, V. S., Sharma, S., Usman, M. J., Gupta, A., & Kumar, V. (2021) · IEEE Access · doi:10.1109/access.2021.3102042 · Cited by 8
“Kaya et al. [35] developed a multi-modal system for emotion recognition in wild.”
BackgroundDew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN
Podder, T., Bhattacharya, D., & Majumdar, A. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2177-3_31 · Cited by 8
Shah, A. K., Kadyan, V., & Kane, L. (2025) · SN Computer Science · doi:10.1007/s42979-025-03861-1 · Cited by 7
“[54] presents a video-based multi-modal solution for emotion recognition in the wild.”
Bakhshi, A., Wong, A. S. W., & Chalup, S. (2020) · European Conference on Artificial Intelligence · doi:10.3233/faia200190 · Cited by 8
“Combination of different modalities such as physiological signals [20], audio [48, 26], video [12], hand gestures [24], and body movement [6] leads to many advancements in the field of affect recognition such as safe driving, security, and mobile health [22].”
BackgroundFear Facial Emotion Recognition Based on Angular Deviation
Fnaiech, A., Sahli, H., Sayadi, M., & Gorce, P. (2021) · Electronics · doi:10.3390/electronics10030358 · Cited by 7
“[12] projected a multimodal expression recognition approach in the wild employing deep transfer learning and score fusion.”
BackgroundCross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition
Xu, Y., Jiang, X., & Wu, D. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3366767 · Cited by 6
“Pre-training on large datasets of relevant (but may not be exactly the same) tasks and then fine-tuning on task-specific data was used in facial emotion recognition [20], [21], [22].”
BackgroundAli, M. S., Motin, M. A., & Kabir, S. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3607767 · Cited by 6
“Stress can be monitored through various approaches, including facial expression analysis, text-based assessments, and physiological signal analysis [3], [4], [5].”
Speech Emotion Recognition by Combining a Unified First-Order Attention Network With Data Balance
Chen, G., Zhang, S., Tao, X., & Zhao, X. (2020) · IEEE Access · doi:10.1109/access.2020.3038493 · Cited by 6
Designing Computational Tools for Behavioral and Clinical Science
Salah, A. A. (2021) · ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS) · doi:10.1145/3459926.3464906 · Cited by 5
“They don’t only focus on the analysis of the individual behavior or an interaction from multiple modalities [14, 33], but also include analysis of the context of behavior, which can be culturally-conditioned, or highly domain-”
BackgroundSelf-citationFacial Expression Recognition Using Deep Learning Methods
Kardakis, S., Perikos, I., Grivokostopoulou, F., & Paraskevas, M. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-92317-4_13 · Cited by 6
Recognizing facial emotions for educational learning settings
Akputu, O. K., Inyang, U. G., Msugh, O., Mughal, F., & Usoro, A. (2022) · IAES International Journal of Robotics and Automation (IJRA) · doi:10.11591/ijra.v11i1.pp21-32 · Cited by 6
Multimodal emotion recognition using deep learning techniques
Nguyen, T. D. (2020) · Queensland University of Technology · doi:10.5204/thesis.eprints.180753 · Cited by 6
A Systematic Evaluation of Domain Adaptation in Facial Expression Recognition.Preprint
Kong, Y., Suresh, V., Soh, J. M., & Ong, D. C. (2021) · arXiv (Cornell University) · Cited by 6
Zhou, Y., Yang, B. Q., Liu, Z., Wang, Q., & Xiong, P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18713-y · Cited by 5
“[37] also obtained the correlated emotional features via transfer learning and illustrate the importance of fine-tuning process.”
The Fuzzy Emotion Recognition Framework Using Semantic-Linguistic Facial Features
Liliana, D. Y., & Basaruddin, T. (2019) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc47129.2019.9042442 · Cited by 5
“Fuzzy emotion rules are developed from the knowledge of psychologists drawn from Ekman's theory [2], [3], as well as from observations and interviews with psychologists.”
BackgroundHierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
Jadhav, N. (2022) · Journal of Information Systems and Telecommunication (JIST) · doi:10.52547/jist.16499.10.38.89 · Cited by 5
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion
Zhang, Y., Lu, J., Wang, X., Li, Z., Xiao, R., Wang, W., Li, M., & Zhang, P. (2022) · doi:10.1145/3552466.3556528 · Cited by 6
“If the range of scores changes, the fusion results will not be satisfactory [19].”
Multiview nonlinear discriminant structure learning for emotion recognition
Guo, S., Song, L., Xie, R., Li, L., & Liu, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110042 · Cited by 5
Monitoring Application-driven Continuous Affect Recognition From Video Frames
Hwooi, S. K. W., Othmani, A., & Sabri, A. Q. M. (2023) · International Conference on Image, Video and Signal Processing (IVSP) · doi:10.1145/3591156.3591161 · Cited by 5
“Handcrafted, CNN, and audio features were combined for emotion classification [14].”
MethodOak Leaf Classification: An Analysis of Features and Classifiers
Kaya, H., Keklik, I., Ensarı, T., Alkan, F., & Biricik, Y. (2019) · Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) · doi:10.1109/ebbt.2019.8742053 · Cited by 5
“On the average we do not see a significant performance difference among classifiers, while ELM training is two orders of magnitude faster.”
MethodInfluentialSelf-citationImproving facial emotion recognition through dataset merging and balanced training strategies
Kırbız, S. (2025) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2025.107659 · Cited by 4
“Kaya et al. [38] showed that VGG-Face trained on the large-scale ImageNet dataset and fine-tuned on the FER2013 dataset, improves the accuracy of FER.”
Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
Zhou, L. (2021) · Discrete Dynamics in Nature and Society · doi:10.1155/2021/1272502 · Cited by 4
RetractedVideo-Based Emotion Recognition in the Wild for Online Education Systems
Mai, G., Guo, Z., She, Y., Wang, H., & Liang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20868-3_38 · Cited by 4
Expression recognition based on residual rectification convolution neural network
Chen, B., Zhu, J.-N., & Dong, Y.-Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12159-w · Cited by 4
Ashraf, A., Gunawan, T. S., Arifin, F., Kartiwi, M., Sophian, A., & Habaebi, M. H. (2022) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v10i3.3879 · Cited by 4
Multi-modal Arousal and Valence Estimation under Noisy Conditions
Dresvyanskiy, D., Markitantov, M., Yu, J., Kaya, H., & Karpov, A. A. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00480 · Cited by 4
“This approach is shown to generalize well to in-the-wild data [12, 23].”
Self-citationHeterogeneous Non-Local Fusion for Multimodal Activity Recognition
Byvshev, P., Mettes, P., & Xiao, Y. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390675 · Cited by 4
“When shapes and topologies also do not match, a late fusion of final scores from independently trained single-modal networks is performed [33, 55].”
BackgroundMultimodal Big Data Affective Analytics
Shoumy, N. J., Ang, L.-M., & Rahaman, D. M. M. (2019) · Springer eBooks · doi:10.1007/978-3-319-97598-6_3 · 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
CLBPGNN Convoluted Local Binary Pattern based Grouping Neural Network for Face Emotion Recognition
G., L. P. G., & Krithika, L. B. (2017) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.106.11 · Cited by 3
Pranathi, K., Jagini, N. P., Ramaraj, S. K., & Jeyaraman, D. (2022) · ACTA IMEKO · doi:10.21014/acta_imeko.v11i2.1174 · Cited by 3
Sparse Spatial-Temporal Emotion Graph Convolutional Network for Video Emotion Recognition
Liu, X., Xu, H., & Wang, M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3518879 · Cited by 4
“Kaya et al. [10] use a least squares regression classifier and weighted score fusion to fuse appearance features and audio features.”
Video-based Emotion Recognition using Aggregated Features and Spatio-temporal Information
Xu, J., Dong, Y., Ma, L., & Bai, H. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545441 · Cited by 3
“Convolutional neural networks and deep features have also been used in emotion recognition [26]–[28].”
MethodEnhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons
Ullah, I., Reilly, S., & Madden, M. G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413021 · Cited by 3
“…been used with some modifications in many application areas (for example, pedestrian detection [4], person re-identification [5], semantic segmentation [6], and anomaly detection [7]) by either retraining them from scratch, using transfer learning [8], or fine-tuning them [9] with new datasets.”
MethodVisual 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
“Transfer learning has previously been The dimension of the appearance feature is fixed to 4096. realized based on the VGG-face model [27,29,30].”
BackgroundMethodMiyazawa, A., Thảo, T. P., & Yamaguchi, R. S. (2022) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49033.2022.9700626 · Cited by 3
Audio and Text-Based Emotion Recognition System Using Deep Learning
Thakur, P., Shahu, R., & Gupta, V. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8865-3_41 · Cited by 3
Learning based visual engagement and self-efficacy
Dhamija, S. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273659 · Cited by 3
Guest editorial: Multimodal sentiment analysis and mining in the wild
Soleymani, M., Schuller, B. W., & Chang, S.-F. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.09.001 · Cited by 3
“field and features relevant work addressing multimodal sentiment analysis from different perspectives including emotion recognition methods [7,8], databases of emotional responses [9,10], automatic recognition of visual sentiment [11], applications of text sentiment analysis [12] and determining customer purchase inte…”
BackgroundMethodFacial Expression Recognition Using a Semantic-Based Bottleneck Attention Module
Zhang, S., & Xiao, Z. (2024) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.352418 · Cited by 2
Orynbassar, S., Barkana, D. E., Yershov, E., Nurgaliyev, M., Saymbetov, A., Жоламанов, Б., Dosymbetova, G., Kapparova, A., Koshkarbay, N., Kuttybay, N., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3570785 · Cited by 2
“CNN was used to recognize emotions using thermal imager videos [37], [38], [39].”
Facial Expression Recognition with Small Samples Under Convolutional Neural Network
Cheng, W., Geng, J., & Lin, K. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-04245-4_34 · Cited by 2
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Visualization of Physiological Response in the Context of Emotion Recognition
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Abdallah, T. B., Guermazi, R., & Hammami, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40605-9_43 · Cited by 2
Improving Human Emotion Recognition from Emotive Videos Using Geometric Data Augmentation
Shoumy, N. J., Ang, L.-M., Rahaman, D. M. M., Zia, T. A., Seng, K. P., & Khatun, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79463-7_13 · Cited by 2
Weakly Supervised Learning for Facial Behavior Analysis: A ReviewPreprint
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Detection of Mental Stress From Photoplethysmography for Under- Resourced Healthcare Systems
Ali, M., Motin, M. A., Bipro, S. S., Kabir, S., Syfullah, M. K., & Kumar, D. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3649586 · Cited by 1
“Several approaches have been proposed for stress assessment, such as facial expression analysis, text-based evaluations, and physiological signal analysis [2], [3], [4].”
Koulouris, D., Τζιομάκα, Μ., Zafeiriou, A., Pinitas, K., Kallipolitis, A., Menychtas, A., & Maglogiannis, I. (2025) · South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) · doi:10.1109/seeda-cecnsm68644.2025.11329599 · Cited by 1
Real-Time Emotion Recognition Using Deep Learning Algorithms
Mettiti, A. E., Oumsis, M., Chehri, A., & Saadane, R. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-fall57202.2022.10012772 · Cited by 1
“…enormous analog and parallel computations for information processing and knowledge representation [14]–[20] Facial expression recognition [21]–[24] Voice recognition [25]–[27] Physiological recognition Random forest [28], [29] Voice recognition Random Forest's classification and regression…”
BackgroundIntegrative Sensing Techniques for Building Non-Invasive Body Sensor Networks
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“Dense and audio features were also obtained, and these features were fed into kernel extreme learning machine and partial least squares regression classification models, and applied weighted score fusion for each model and class [12].”
BackgroundMethodInfluentialMeta Transfer Learning for Facial Emotion Recognition
Nguyen, D., Nguyen, K., Sridharan, S., Abbasnejad, I., Dean, D., & Fookes, C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545411 · Cited by 1
BSF-RCNN-VFR: Background Subtracted Faster RCNN for Video based Face Recognition
Seshaiah, M., & Math, S. (2019) · Cited by 1
Fusion of Deep-CNN and Texture Features for Emotion Recognition using Support Vector Machines
Sujanaa, J., & Palanivel, S. (2021) · Cited by 1
BAUFER: A Baseline-Enabled Facial Expression Recognition Pipeline Trained with Limited Annotations
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Reddy, K. A., Regula, T., Sharmila, K., Srinivas, P. V. V. S., & Rahman, S. Z. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280505 · Cited by 1
“Another prevalent hand-crafted method for facial emotion identification is the Local Binary Pattern (LBP) [19, 20], which has also found use in texture classification and various other applications [21, 22].”
Method
Metadata
- Authors
- Kaya, H., Gürpınar, F., & Salah, A. A.
- Affiliations
- Tekirdağ Namık Kemal University (TR)
- Boğaziçi University (TR)
- Corresponding author
- Kaya, Heysem
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 65
- Pages
- 66-75
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2585658440
- Cited by
- 235
- References
- 73
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 13 · 2023: 19 · 2022: 31 · 2021: 37 · 2020: 42 · 2019: 41 · 2018: 26 · 2017: 8
- FWCI
- 41.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Image and Vision Computing (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
- video-based emotion recognition, in-the-wild emotion recognition, multimodal emotion recognition, facial expression recognition, transfer learning, score-level fusion, audiovisual integration, convolutional neural network features, fine-tuning
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