Multi-modal emotion recognition using semi-supervised learning and multiple neural networks in the wild
Explore this paper's citation graph
Summary
This paper proposes a method for classifying human emotions through multiple neural networks based on multi-modal signals which consist of image, landmark, and audio in a wild environment and proposes an audio deep learning mechanism robust to the specific emotions.
- Type
- article
- Published
- 2017-11-03
- Cited by
- 51
- References
- 42
- OpenAlex
- https://openalex.org/W2767915528
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:30321809
Keywords
Computer science, Artificial intelligence, Landmark, Artificial neural network, Pattern recognition (psychology)
References
- Learning Spatiotemporal Features with 3D Convolutional Networks
- Training Deeper Convolutional Networks with Deep Supervision
- Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A convolutional neural network cascade for face detection
- An argument for basic emotions
- Collecting Large, Richly Annotated Facial-Expression Databases from Movies
- A comparative study of texture measures with classification based on featured distributions
- Opensmile: the munich versatile and fast open-source audio feature extractor
- Going deeper with convolutions
- Performance evaluation of texture measures with classification based on Kullback discrimination of distributions
- The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
- Recognizing Action Units for Facial Expression Analysis
- ImageNet: A large-scale hierarchical image database
- Support-Vector Networks
- Incremental Face Alignment in the Wild
- EEG-Based Emotion Recognition in Music Listening
- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
- Handwritten Digit Recognition with a Back-Propagation Network
- Histograms of oriented gradients for human detection
Cited by
- Deep Facial Expression Recognition: A Survey
- Audio-based emotion recognition using GMM supervector an SVM linear kernel
- Video-based Emotion Recognition Using Deeply-Supervised Neural Networks
- Multilevel Sensor Fusion With Deep Learning
- Deep Transfer Learning for Emotion Recognition Networks
- Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition
- Semi-Supervised Speech Emotion Recognition With Ladder Networks
- Twosome Modelling based Emotion Recognition in Videos
- Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network
- Visual Scene-aware Hybrid Neural Network Architecture for Video-based Facial Expression Recognition
- Feature-Level and Model-Level Audiovisual Fusion for Emotion Recognition in the Wild
- Facial Expression Recognition with Skip-Connection to Leverage Low-Level Features
- Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network
- Multi-modal Correlated Network for emotion recognition in speech
- Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels. (Deep learning for multimodal and temporal contents analysis)
- Semi-Supervised Learning for Continuous Emotion Recognition Based on Metric Learning
- TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
- Semi-supervised learning for facial expression-based emotion recognition in the continuous domain
- Visual Scene-Aware Hybrid and Multi-Modal Feature Aggregation for Facial Expression Recognition
- Multimodal Attention Network for Continuous-Time Emotion Recognition Using Video and EEG Signals
Related papers
- Skin Disease Diagnostic techniques using deep learning
- Real-Time Object Recognition for Football Field Landmark Detection Based on Deep Neural Networks
- Efficient Disease Risk Prediction based on Deep Learning Approach
- A Survey on Deep Learning Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification
- A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network
- Face Alignment by Discriminative Feature Learning
- Machine Learning and Deep Learning applications-a vision using the SPSS Method
- Detecting Anatomical Landmarks from Limited Medical Imaging Data using Two-Stage Task-Oriented Deep Neural Networks
- Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey
- Optimization of Machine Learning and Deep Learning Algorithms for Diagnosis of Cancer