Video-based emotion recognition using CNN-RNN and C3D hybrid networks
By Yin Fan, Xiangju Lu, Dian Li, Yuanliu Liu. International Conference on Multimodal Interaction, 2016
564 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/92527ace7f75188b5ec209ff7d59f431343075e4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Weihong Deng
- Shan Li
- Behzad Hasani
- Ali Mollahosseini
- Mohammad H. Mahoor
- D. Bulsara
- Viet-Khoa Vo-Ho
- Ngan T. H. Le
In Experimental and Cognitive Psychology
- Weihong Deng
- Shan Li
- Behzad Hasani
- Ali Mollahosseini
- Mohammad H. Mahoor
- Stylianos Asteriadis
- Esam Ghaleb
- Mirela C. Popa
Citations in the corpus
- Spiking Neural Networks and Their Applications: A Review (2022, methodology): “Although ANNs have been remarkably successful in many applications, including object detection [26,27], image segmentation [28–30], and action recognition [31,32], they are still limited in the way they deal with temporal information.”
- Deep Facial Expression Recognition: A Survey (2018, methodology): “[108] C3D 10 3 - - several windows of 16 consecutive frames Validation: 39.”
- Context-Aware Emotion Recognition Networks (2019, unknown): “Various techniques to capture temporal dynamics in videos have also been proposed making connections across the time using recurrent neural networks (RNNs) or deep 3D-CNNs [19, 20].”
- Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, background): “86 sentiment classification; [37] in video-based emotion recognition; [38] in phrase-aware sentence classification; and [39] in abnormal event detection from video.”
- AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “[47] AFEW - Trained on both video and audio.”
- Multimodal and Temporal Perception of Audio-visual Cues for Emotion Recognition (2019, background): “In the literature of multimodal emotion recognition, much of the effort went either to a late fusion of modalities [13] or to building temporal features, based on the assumption that emotions are expressed simultaneously and global information can be obtained to represent the emotional content of a ”