Recurrent Neural Networks for Emotion Recognition in Video

By Samira Ebrahimi Kahou, Vincent Michalski, Kishore Reddy Konda, Roland Memisevic, C. Pal. International Conference on Multimodal Interaction, 2015

393 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/300fd9ae3bb33fba3e48b605e49d59b3ce3957ff

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019, background): “unsupervised SER, speech recognition and NLP [132], [133].”
Current challenges and visions in music recommender systems research (2017, background): “The implicit acquisition of emotional states can be effected, for instance, by analyzing user-generated text [49], speech [66], or facial expressions in video [55].”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “CNN-LSTM (Ebrahimi Kahou et al., 2015) is a recurrent model that at each timestamp performs convolutions over facial region and uses output to an LSTM. Face processing is similar to 3DCNN. LSTM-FA similar to both baselines above, information extracted by FACET is used every 6 frames as input to an…”
Video-based emotion recognition using CNN-RNN and C3D hybrid networks (2016, methodology): “, ) n z z z via the following equations [5, 8]:”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, background): “Several researchers attempt to utilize RNN to solve the problem of expression recognition [31]–[33].”
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017, background): “[21] combined CNNs and RNNs to recognize categorical emotions in videos.”
Deep Facial Expression Recognition: A Survey (2018, methodology): “Derived from RNN, an RNN that is composed of ReLUs and initialized with the identity matrix (IRNN) [160] was used to provide a simple mechanism to address the exploding and vanishing gradient problems [84].”
A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, background): “Traditionally, human facial emotions have been studied using either 2D static images or 2D video sequences.”