The DOI matches its Crossref record; 68 of the 73 references it cites were found in a registry or an index.
Video-based emotion recognition in the wild using deep transfer learning and score fusion
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“For example the large domain gap with the objection net [15], and subject identification information in [11].”
BackgroundAlencar, J. P., Mendes, G. M. P., Mattos, C. L. C., Gomes, J. P. P., Babadopulos, L. F. D. A. L., Soares, J. B., & Veras, M. B. A. (2024) · Research Square · doi:10.21203/rs.3.rs-4437158/v1 · Cited by 1
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Computer vision based smoke and fire detection for outdoor environments
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Learning Sequential Patterns Using Spatio-Temporal Attention Networks for Video ClassificationPreprint
Deshmukh, S., Patwardhan, M. S., Mahajan, A., & Deshpande, S. (2021) · Research Square · doi:10.21203/rs.3.rs-148673/v1
“In this paper, we have designed the architectures to generalize the algorithm and identify the most contributing frames using the selfattention mechanism [9].”
BackgroundMethodβ-Band Analysis from Simulated Flight Experiments
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“A multisensing approach can also be applied in several research contexts, for instance, in aviation, using flight simulations [7]; using summarizing functions of complementary visual descriptors for video modeling [17]; identification of cognitive states of aircraft pilots while they are using flight simulators [18,19…”
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“The novel approach of transfer learning includes obtaining high-level features with the help of a CNN trained on huge sources of data [18,19,20]or transferring the knowledge from greater quality auxiliary image datasets.”
Research on emotion recognition method of police dogs based on deep learning
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“…methods of identifying and defining universal expressions [1] to the current cutting-edge methods of automatic recognition of animals' emotions in pictures or videos based on deep learning [2] , it has been shown that it is possible to reliably and accurately infer animal emotions from expressions.”
BackgroundOpen-Set Recognition for Facial-Expression Recognition
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“For instance, in [13], the VGG-face network that was implemented to solve the face recognition problem is used to develop the facial emotion recognition problem.”
MethodPENERAPAN CONVOLUTIONAL NEURAL NETWORK DALAM ANALISIS EMOSI
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Affective Computing in Active Assisted Living
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Self-citationELG: Emotion Recognition Convolutional Model Integrating Local and Global Facial Features
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“For example, [35] fine-tuned the VGG-Face model—originally trained for face recognition— on the FER 2013 dataset.”
From Rivers to Floodplains: Leveraging Transfer Learning to Predict Floodplain Dissolved Oxygen
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“…al., 2021; Feng & Chaspari, 2020; Han et al., 2019; Perera & Patel, 2019), natural language processing (Feng & Chaspari, 2020; Gideon et al., 2017; Kaya et al., 2017; Raffel et al., 2020; Ruder et al., 2019), and medical imaging (Tajbakhsh et al., 2016; Wang et al., 2021), and has been proven to…”
Behaviour Analysis of Students and Predicting Their Academic ResultPreprint
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Bias-Aware Method for Combining Audio and Video Data in Personality Assessment AI Models
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“A well-known dataset is the First Impressions V2 Dataset (ChaLearn CVPR 2017), which provides 10,000 short video clips and crowdsourced Big Five trait labels [2].”
InfluentialUsing ITS and AI Models for Analysing Student Behaviour and Predicting Their Academic OutcomePreprint
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Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels
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“, [99, 146]) et utilise un modèle VGG [217] pré-entraîné à la reconnaissance d’identité.”
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Impact of Learning Rate Schedulers on ResNet Optimization for Facial Emotion Recognition
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“In their study, Kaya, Gürpınar, and Salah [14] explored the concept of emotion recognition in real-life scenarios using deep transfer learning approaches.”
TriCe: Tri-stream video-based emotion change estimation framework
Thuseethan, S., Rajasegarar, S., Yearwood, J. L., Shanmugam, B., & Sebastian, Y. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12241-6
“The method in [51] is a cascaded CNN and bidirectional RNN (BRNN) based architecture, which is used as an emotion recognizer.”
Ifti, S., Rony, R. J., Chakraborty, S., Mahbub, A., & Ahmed, N. (2026) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3811242.3819996
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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